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### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Normalising Flows (NFs) are a class of likelihood-based generative models that have recently gained popularity.', 'They are based on the idea of transforming a simple density into that of the data.', 'We seek to better understand this class of models, and how they compare to previously proposed techniques for generative modeling and unsupervised representation learning.', 'For this purpose we reinterpret NFs in the framework of Variational Autoencoders (VAEs), and present a new form of VAE that generalises normalising flows.', 'The new generalised model also reveals a close connection to denoising autoencoders, and we therefore call our model the Variational Denoising Autoencoder (VDAE).', 'Using our unified model, we systematically examine the model space between flows, variational autoencoders, and denoising autoencoders, in a set of preliminary experiments on the MNIST handwritten digits.', 'The experiments shed light on the modeling assumptions implicit in these models, and they suggest multiple new directions for future research in this space.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['We investigate methods to efficiently learn diverse strategies in reinforcement learning for a generative structured prediction problem: query reformulation.', 'In the proposed framework an agent consists of multiple specialized sub-agents and a meta-agent that learns to aggregate the answers from sub-agents to produce a final answer.', 'Sub-agents are trained on disjoint partitions of the training data, while the meta-agent is trained on the full training set.', 'Our method makes learning faster, because it is highly parallelizable, and has better generalization performance than strong baselines, such as\n', 'an ensemble of agents trained on the full data.', 'We evaluate on the tasks of document retrieval and question answering.', 'The\n', 'improved performance seems due to the increased diversity of reformulation strategies.', 'This suggests that multi-agent, hierarchical approaches might play an important role in structured prediction tasks of this kind.', 'However, we also find that it is not obvious how to characterize diversity in this context, and a first attempt based on clustering did not produce good results.', 'Furthermore, reinforcement learning for the reformulation task is hard in high-performance regimes.', 'At best, it only marginally improves over the state of the art, which highlights the complexity of training models in this framework for end-to-end language understanding problems.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['We study continuous action reinforcement learning problems in which it is crucial that the agent interacts with the environment only through safe policies, i.e.,~policies that keep the agent in desirable situations, both during training and at convergence.', 'We formulate these problems as {\\em constrained} Markov decision processes (CMDPs) and present safe policy optimization algorithms that are based on a Lyapunov approach to solve them.', 'Our algorithms can use any standard policy gradient (PG) method, such as deep deterministic policy gradient (DDPG) or proximal policy optimization (PPO), to train a neural network policy, while guaranteeing near-constraint satisfaction for every policy update by projecting either the policy parameter or the selected action onto the set of feasible solutions induced by the state-dependent linearized Lyapunov constraints.', 'Compared to the existing constrained PG algorithms, ours are more data efficient as they are able to utilize both on-policy and off-policy data.', 'Moreover, our action-projection algorithm often leads to less conservative policy updates and allows for natural integration into an end-to-end PG training pipeline.', 'We evaluate our algorithms and compare them with the state-of-the-art baselines on several simulated (MuJoCo) tasks, as well as a real-world robot obstacle-avoidance problem, demonstrating their effectiveness in terms of balancing performance and constraint satisfaction.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Generative networks are known to be difficult to assess.', 'Recent works on generative models, especially on generative adversarial networks, produce nice samples of varied categories of images.', 'But the validation of their quality is highly dependent on the method used.', 'A good generator should generate data which contain meaningful and varied information and that fit the distribution of a dataset.', 'This paper presents a new method to assess a generator.', 'Our approach is based on training a classifier with a mixture of real and generated samples.', 'We train a generative model over a labeled training set, then we use this generative model to sample new data points that we mix with the original training data.', 'This mixture of real and generated data is thus used to train a classifier which is afterwards tested on a given labeled test dataset.', 'We compare this result with the score of the same classifier trained on the real training data mixed with noise.', "By computing the classifier's accuracy with different ratios of samples from both distributions (real and generated) we are able to estimate if the generator successfully fits and is able to generalize the distribution of the dataset.", 'Our experiments compare the result of different generators from the VAE and GAN framework on MNIST and fashion MNIST dataset.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['We propose Automating Science Journalism (ASJ), the process of producing a press release from a scientific paper, as a novel task that can serve as a new benchmark for neural abstractive summarization.', 'ASJ is a challenging task as it requires long source texts to be summarized to long target texts, while also paraphrasing complex scientific concepts to be understood by the general audience.', 'For this purpose, we introduce a specialized dataset for ASJ that contains scientific papers and their press releases from Science Daily.', 'While state-of-the-art sequence-to-sequence (seq2seq) models could easily generate convincing press releases for ASJ, these are generally nonfactual and deviate from the source.', 'To address this issue, we improve seq2seq generation via transfer learning by co-training with new targets:', '(i) scientific abstracts of sources and', '(ii) partitioned press releases.', 'We further design a measure for factuality that scores how pertinent to the scientific papers the press releases under our seq2seq models are.', 'Our quantitative and qualitative evaluation shows sizable improvements over a strong baseline, suggesting that the proposed framework could improve seq2seq summarization beyond ASJ.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ["The interpretability of an AI agent's behavior is of utmost importance for effective human-AI interaction.", 'To this end, there has been increasing interest in characterizing and generating interpretable behavior of the agent.', "An alternative approach to guarantee that the agent generates interpretable behavior would be to design the agent's environment such that uninterpretable behaviors are either prohibitively expensive or unavailable to the agent.", 'To date, there has been work under the umbrella of goal or plan recognition design exploring this notion of environment redesign in some specific instances of interpretable of behavior.', 'In this position paper, we scope the landscape of interpretable behavior and environment redesign in all its different flavors.', 'Specifically, we focus on three specific types of interpretable behaviors -- explicability, legibility, and predictability -- and present a general framework for the problem of environment design that can be instantiated to achieve each of the three interpretable behaviors.', 'We also discuss how specific instantiations of this framework correspond to prior works on environment design and identify exciting opportunities for future work.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Inference models, which replace an optimization-based inference procedure with a learned model, have been fundamental in advancing Bayesian deep learning, the most notable example being variational auto-encoders (VAEs).', 'In this paper, we propose iterative inference models, which learn how to optimize a variational lower bound through repeatedly encoding gradients.', 'Our approach generalizes VAEs under certain conditions, and by viewing VAEs in the context of iterative inference, we provide further insight into several recent empirical findings.', 'We demonstrate the inference optimization capabilities of iterative inference models, explore unique aspects of these models, and show that they outperform standard inference models on typical benchmark data sets.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['In artificial neural networks trained with gradient descent, the weights used for processing stimuli are also used during backward passes to calculate gradients.', 'For the real brain to approximate gradients, gradient information would have to be propagated separately, such that one set of synaptic weights is used for processing and another set is used for backward passes.', 'This produces the so-called "weight transport problem" for biological models of learning, where the backward weights used to calculate gradients need to mirror the forward weights used to process stimuli.', 'This weight transport problem has been considered so hard that popular proposals for biological learning assume that the backward weights are simply random, as in the feedback alignment algorithm.', 'However, such random weights do not appear to work well for large networks.', 'Here we show how the discontinuity introduced in a spiking system can lead to a solution to this problem.', 'The resulting algorithm is a special case of an estimator used for causal inference in econometrics, regression discontinuity design.', 'We show empirically that this algorithm rapidly makes the backward weights approximate the forward weights.', 'As the backward weights become correct, this improves learning performance over feedback alignment on tasks such as Fashion-MNIST and CIFAR-10.', 'Our results demonstrate that a simple learning rule in a spiking network can allow neurons to produce the right backward connections and thus solve the weight transport problem.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Variational inference (VI) methods and especially variational autoencoders (VAEs) specify scalable generative models that enjoy an intuitive connection to manifold learning --- with many default priors the posterior/likelihood pair $q(z|x)$/$p(x|z)$ can be viewed as an approximate homeomorphism (and its inverse) between the data manifold and a latent Euclidean space.', 'However, these approximations are well-documented to become degenerate in training.', 'Unless the subjective prior is carefully chosen, the topologies of the prior and data distributions often will not match.\n', 'Conversely, diffusion maps (DM) automatically \\textit{infer} the data topology and enjoy a rigorous connection to manifold learning, but do not scale easily or provide the inverse homeomorphism.\n', 'In this paper, we propose \\textbf{a)} a principled measure for recognizing the mismatch between data and latent distributions and \\textbf{b)} a method that combines the advantages of variational inference and diffusion maps to learn a homeomorphic generative model.', 'The measure, the \\textit{locally bi-Lipschitz property}, is a sufficient condition for a homeomorphism and easy to compute and interpret.', 'The method, the \\textit{variational diffusion autoencoder} (VDAE), is a novel generative algorithm that first infers the topology of the data distribution, then models a diffusion random walk over the data.', 'To achieve efficient computation in VDAEs, we use stochastic versions of both variational inference and manifold learning optimization.', 'We prove approximation theoretic results for the dimension dependence of VDAEs, and that locally isotropic sampling in the latent space results in a random walk over the reconstructed manifold.\n', 'Finally, we demonstrate the utility of our method on various real and synthetic datasets, and show that it exhibits performance superior to other generative models.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['While deep learning and deep reinforcement learning systems have demonstrated impressive results in domains such as image classification, game playing, and robotic control, data efficiency remains a major challenge, particularly as these algorithms learn individual tasks from scratch.', 'Multi-task learning has emerged as a promising approach for sharing structure across multiple tasks to enable more efficient learning.', 'However, the multi-task setting presents a number of optimization challenges, making it difficult to realize large efficiency gains compared to learning tasks independently.', 'The reasons why multi-task learning is so challenging compared to single task learning are not fully understood.', 'Motivated by the insight that gradient interference causes optimization challenges, we develop a simple and general approach for avoiding interference between gradients from different tasks, by altering the gradients through a technique we refer to as “gradient surgery”.', 'We propose a form of gradient surgery that projects the gradient of a task onto the normal plane of the gradient of any other task that has a conflicting gradient.', 'On a series of challenging multi-task supervised and multi-task reinforcement learning problems, we find that this approach leads to substantial gains in efficiency and performance. ', 'Further, it can be effectively combined with previously-proposed multi-task architectures for enhanced performance in a model-agnostic way.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['In this paper we propose to view the acceptance rate of the Metropolis-Hastings algorithm as a universal objective for learning to sample from target distribution -- given either as a set of samples or in the form of unnormalized density.', 'This point of view unifies the goals of such approaches as Markov Chain Monte Carlo (MCMC), Generative Adversarial Networks (GANs), variational inference.', 'To reveal the connection we derive the lower bound on the acceptance rate and treat it as the objective for learning explicit and implicit samplers.', 'The form of the lower bound allows for doubly stochastic gradient optimization in case the target distribution factorizes (i.e. over data points).', 'We empirically validate our approach on Bayesian inference for neural networks and generative models for images.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['This paper proposes a self-supervised learning approach for video features that results in significantly improved performance on downstream tasks (such as video classification, captioning and segmentation) compared to existing methods.', 'Our method extends the BERT model for text sequences to the case of sequences of real-valued feature vectors, by replacing the softmax loss with noise contrastive estimation (NCE).', 'We also show how to learn representations from sequences of visual features and sequences of words derived from ASR (automatic speech recognition), and show that such cross-modal training (when possible) helps even more.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['We present a generic dynamic architecture that employs a problem specific differentiable forking mechanism to leverage discrete logical information about the problem data structure.', 'We adapt and apply our model to CLEVR Visual Question Answering, giving rise to the DDRprog architecture; compared to previous approaches, our model achieves higher accuracy in half as many epochs with five times fewer learnable parameters.', 'Our model directly models underlying question logic using a recurrent controller that jointly predicts and executes functional neural modules; it explicitly forks subprocesses to handle logical branching.', 'While FiLM and other competitive models are static architectures with less supervision, we argue that inclusion of program labels enables learning of higher level logical operations -- our architecture achieves particularly high performance on questions requiring counting and integer comparison. We further demonstrate the generality of our approach though DDRstack -- an application of our method to reverse Polish notation expression evaluation in which the inclusion of a stack assumption allows our approach to generalize to long expressions, significantly outperforming an LSTM with ten times as many learnable parameters.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['We propose Support-guided Adversarial Imitation Learning (SAIL), a generic imitation learning framework that unifies support estimation of the expert policy with the family of Adversarial Imitation Learning (AIL) algorithms.', 'SAIL addresses two important challenges of AIL, including the implicit reward bias and potential training instability.', 'We also show that SAIL is at least as efficient as standard AIL.', 'In an extensive evaluation, we demonstrate that the proposed method effectively handles the reward bias and achieves better performance and training stability than other baseline methods on a wide range of benchmark control tasks.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['We consider the task of few shot link prediction, where the goal is to predict missing edges across multiple graphs using only a small sample of known edges.', 'We show that current link prediction methods are generally ill-equipped to handle this task---as they cannot effectively transfer knowledge between graphs in a multi-graph setting and are unable to effectively learn from very sparse data.', 'To address this challenge, we introduce a new gradient-based meta learning framework, Meta-Graph, that leverages higher-order gradients along with a learned graph signature function that conditionally generates a graph neural network initialization.', 'Using a novel set of few shot link prediction benchmarks, we show that Meta-Graph enables not only fast adaptation but also better final convergence and can effectively learn using only a small sample of true edges.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Generative neural networks map a standard, possibly distribution to a complex high-dimensional distribution, which represents the real world data set.', 'However, a determinate input distribution as well as a specific architecture of neural networks may impose limitations on capturing the diversity in the high dimensional target space.', 'To resolve this difficulty, we propose a training framework that greedily produce a series of generative adversarial networks that incrementally capture the diversity of the target space.', "We show theoretically and empirically that our training algorithm converges to the theoretically optimal distribution, the projection of the real distribution onto the convex hull of the network's distribution space."] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Generative priors have become highly effective in solving inverse problems including denoising, inpainting, and reconstruction from few and noisy measurements.', 'With a generative model we can represent an image with a much lower dimensional latent codes.', 'In the context of compressive sensing, if the unknown image belongs to the range of a pretrained generative network, then we can recover the image by estimating the underlying compact latent code from the available measurements.', 'However, recent studies revealed that even untrained deep neural networks can work as a prior for recovering natural images.', 'These approaches update the network weights keeping latent codes fixed to reconstruct the target image from the given measurements.', 'In this paper, we optimize over network weights and latent codes to use untrained generative network as prior for video compressive sensing problem.', 'We show that by optimizing over latent code, we can additionally get concise representation of the frames which retain the structural similarity of the video frames.', 'We also apply low-rank constraint on the latent codes to represent the video sequences in even lower dimensional latent space.', 'We empirically show that our proposed methods provide better or comparable accuracy and low computational complexity compared to the existing methods.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Magnitude-based pruning is one of the simplest methods for pruning neural networks.', 'Despite its simplicity, magnitude-based pruning and its variants demonstrated remarkable performances for pruning modern architectures.', 'Based on the observation that the magnitude-based pruning indeed minimizes the Frobenius distortion of a linear operator corresponding to a single layer, we develop a simple pruning method, coined lookahead pruning, by extending the single layer optimization to a multi-layer optimization.', 'Our experimental results demonstrate that the proposed method consistently outperforms the magnitude pruning on various networks including VGG and ResNet, particularly in the high-sparsity regime.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Recent literature has demonstrated promising results on the training of Generative Adversarial Networks by employing a set of discriminators, as opposed to the traditional game involving one generator against a single adversary.', 'Those methods perform single-objective optimization on some simple consolidation of the losses, e.g. an average.', 'In this work, we revisit the multiple-discriminator approach by framing the simultaneous minimization of losses provided by different models as a multi-objective optimization problem.', 'Specifically, we evaluate the performance of multiple gradient descent and the hypervolume maximization algorithm on a number of different datasets.', 'Moreover, we argue that the previously proposed methods and hypervolume maximization can all be seen as variations of multiple gradient descent in which the update direction computation can be done efficiently.', 'Our results indicate that hypervolume maximization presents a better compromise between sample quality and diversity, and computational cost than previous methods.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Designing of search space is a critical problem for neural architecture search (NAS) algorithms.', 'We propose a fine-grained search space comprised of atomic blocks, a minimal search unit much smaller than the ones used in recent NAS algorithms.', 'This search space facilitates direct selection of channel numbers and kernel sizes in convolutions.', 'In addition, we propose a resource-aware architecture search algorithm which dynamically selects atomic blocks during training.', 'The algorithm is further accelerated by a dynamic network shrinkage technique.\n', 'Instead of a search-and-retrain two-stage paradigm, our method can simultaneously search and train the target architecture in an end-to-end manner. \n', 'Our method achieves state-of-the-art performance under several FLOPS configurations on ImageNet with a negligible searching cost.\n', 'We open our entire codebase at: https://github.com/meijieru/AtomNAS.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['We introduce Lyceum, a high-performance computational ecosystem for robotlearning. ', 'Lyceum is built on top of the Julia programming language and theMuJoCo physics simulator, combining the ease-of-use of a high-level program-ming language with the performance of native C. Lyceum is up to 10-20Xfaster compared to other popular abstractions like OpenAI’sGymand Deep-Mind’sdm-control. ', 'This substantially reduces training time for various re-inforcement learning algorithms; and is also fast enough to support real-timemodel predictive control with physics simulators. ', 'Lyceum has a straightfor-ward API and supports parallel computation across multiple cores or machines.', 'The code base, tutorials, and demonstration videos can be found at: https://sites.google.com/view/lyceum-anon.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['There is a strong incentive to develop versatile learning techniques that can transfer the knowledge of class-separability from a labeled source domain to an unlabeled target domain in the presence of a domain-shift.', 'Existing domain adaptation (DA) approaches are not equipped for practical DA scenarios as a result of their reliance on the knowledge of source-target label-set relationship (e.g. Closed-set, Open-set or Partial DA).', 'Furthermore, almost all the prior unsupervised DA works require coexistence of source and target samples even during deployment, making them unsuitable for incremental, real-time adaptation.', 'Devoid of such highly impractical assumptions, we propose a novel two-stage learning process.', 'Initially, in the procurement-stage, the objective is to equip the model for future source-free deployment, assuming no prior knowledge of the upcoming category-gap and domain-shift.', 'To achieve this, we enhance the model’s ability to reject out-of-source distribution samples by leveraging the available source data, in a novel generative classifier framework.', 'Subsequently, in the deployment-stage, the objective is to design a unified adaptation algorithm capable of operating across a wide range of category-gaps, with no access to the previously seen source samples.', 'To achieve this, in contrast to the usage of complex adversarial training regimes, we define a simple yet effective source-free adaptation objective by utilizing a novel instance-level weighing mechanism, named as Source Similarity Metric (SSM).', 'A thorough evaluation shows the practical usability of the proposed learning framework with superior DA performance even over state-of-the-art source-dependent approaches.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['One of the long-standing challenges in Artificial Intelligence for learning goal-directed behavior is to build a single agent which can solve multiple tasks.', 'Recent progress in multi-task learning for goal-directed sequential problems has been in the form of distillation based learning wherein a student network learns from multiple task-specific expert networks by mimicking the task-specific policies of the expert networks.', 'While such approaches offer a promising solution to the multi-task learning problem, they require supervision from large expert networks which require extensive data and computation time for training.\n', 'In this work, we propose an efficient multi-task learning framework which solves multiple goal-directed tasks in an on-line setup without the need for expert supervision.', 'Our work uses active learning principles to achieve multi-task learning by sampling the harder tasks more than the easier ones.', 'We propose three distinct models under our active sampling framework.', 'An adaptive method with extremely competitive multi-tasking performance.', 'A UCB-based meta-learner which casts the problem of picking the next task to train on as a multi-armed bandit problem.', 'A meta-learning method that casts the next-task picking problem as a full Reinforcement Learning problem and uses actor-critic methods for optimizing the multi-tasking performance directly.', 'We demonstrate results in the Atari 2600 domain on seven multi-tasking instances: three 6-task instances, one 8-task instance, two 12-task instances and one 21-task instance.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Numerous machine reading comprehension (MRC) datasets often involve manual annotation, requiring enormous human effort, and hence the size of the dataset remains significantly smaller than the size of the data available for unsupervised learning.', 'Recently, researchers proposed a model for generating synthetic question-and-answer data from large corpora such as Wikipedia.', 'This model is utilized to generate synthetic data for training an MRC model before fine-tuning it using the original MRC dataset.', 'This technique shows better performance than other general pre-training techniques such as language modeling, because the characteristics of the generated data are similar to those of the downstream MRC data.', 'However, it is difficult to have high-quality synthetic data comparable to human-annotated MRC datasets.', 'To address this issue, we propose Answer-containing Sentence Generation (ASGen), a novel pre-training method for generating synthetic data involving two advanced techniques, (1) dynamically determining K answers and (2) pre-training the question generator on the answer-containing sentence generation task.', 'We evaluate the question generation capability of our method by comparing the BLEU score with existing methods and test our method by fine-tuning the MRC model on the downstream MRC data after training on synthetic data.', 'Experimental results show that our approach outperforms existing generation methods and increases the performance of the state-of-the-art MRC models across a range of MRC datasets such as SQuAD-v1.1, SQuAD-v2.0, KorQuAD and QUASAR-T without any architectural modifications to the original MRC model.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Deep neural networks (DNNs) dominate current research in machine learning.', 'Due to massive GPU parallelization DNN training is no longer a bottleneck, and large models with many parameters and high computational effort lead common benchmark tables.', 'In contrast, embedded devices have a very limited capability.', 'As a result, both model size and inference time must be significantly reduced if DNNs are to achieve suitable performance on embedded devices.\n', 'We propose a soft quantization approach to train DNNs that can be evaluated using pure fixed-point arithmetic.', 'By exploiting the bit-shift mechanism, we derive fixed-point quantization constraints for all important components, including batch normalization and ReLU.', 'Compared to floating-point arithmetic, fixed-point calculations significantly reduce computational effort whereas low-bit representations immediately decrease memory costs.', 'We evaluate our approach with different architectures on common benchmark data sets and compare with recent quantization approaches.', 'We achieve new state of the art performance using 4-bit fixed-point models with an error rate of 4.98% on CIFAR-10.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['\tWe present a new approach and a novel architecture, termed WSNet, for learning compact and efficient deep neural networks.', 'Existing approaches conventionally learn full model parameters independently and then compress them via \\emph{ad hoc} processing such as model pruning or filter factorization.', 'Alternatively, WSNet proposes learning model parameters by sampling from a compact set of learnable parameters, which naturally enforces {parameter sharing} throughout the learning process.', 'We demonstrate that such a novel weight sampling approach (and induced WSNet) promotes both weights and computation sharing favorably.', 'By employing this method, we can more efficiently learn much smaller networks with competitive performance compared to baseline networks with equal numbers of convolution filters.', 'Specifically, we consider learning compact and efficient 1D convolutional neural networks for audio classification.', 'Extensive experiments on multiple audio classification datasets verify the effectiveness of WSNet.', 'Combined with weight quantization, the resulted models are up to \\textbf{180$\\times$} smaller and theoretically up to \\textbf{16$\\times$} faster than the well-established baselines, without noticeable performance drop.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Recent work in adversarial machine learning started to focus on the visual perception in autonomous driving and studied Adversarial Examples (AEs) for object detection models.', 'However, in such visual perception pipeline the detected objects must also be tracked, in a process called Multiple Object Tracking (MOT), to build the moving trajectories of surrounding obstacles.', 'Since MOT is designed to be robust against errors in object detection, it poses a general challenge to existing attack techniques that blindly target objection detection: we find that a success rate of over 98% is needed for them to actually affect the tracking results, a requirement that no existing attack technique can satisfy.', 'In this paper, we are the first to study adversarial machine learning attacks against the complete visual perception pipeline in autonomous driving, and discover a novel attack technique, tracker hijacking, that can effectively fool MOT using AEs on object detection.', 'Using our technique, successful AEs on as few as one single frame can move an existing object in to or out of the headway of an autonomous vehicle to cause potential safety hazards.', 'We perform evaluation using the Berkeley Deep Drive dataset and find that on average when 3 frames are attacked, our attack can have a nearly 100% success rate while attacks that blindly target object detection only have up to 25%.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Self-supervised learning (SlfSL), aiming at learning feature representations through ingeniously designed pretext tasks without human annotation, has achieved compelling progress in the past few years.', 'Very recently, SlfSL has also been identified as a promising solution for semi-supervised learning (SemSL) since it offers a new paradigm to utilize unlabeled data.', 'This work further explores this direction by proposing a new framework to seamlessly couple SlfSL with SemSL.', 'Our insight is that the prediction target in SemSL can be modeled as the latent factor in the predictor for the SlfSL target.', 'Marginalizing over the latent factor naturally derives a new formulation which marries the prediction targets of these two learning processes.', 'By implementing this framework through a simple-but-effective SlfSL approach -- rotation angle prediction, we create a new SemSL approach called Conditional Rotation Angle Prediction (CRAP).', 'Specifically, CRAP is featured by adopting a module which predicts the image rotation angle \\textbf{conditioned on the candidate image class}.', 'Through experimental evaluation, we show that CRAP achieves superior performance over the other existing ways of combining SlfSL and SemSL.', 'Moreover, the proposed SemSL framework is highly extendable.', 'By augmenting CRAP with a simple SemSL technique and a modification of the rotation angle prediction task, our method has already achieved the state-of-the-art SemSL performance.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Ranking is a central task in machine learning and information retrieval.', 'In this task, it is especially important to present the user with a slate of items that is appealing as a whole.', 'This in turn requires taking into account interactions between items, since intuitively, placing an item on the slate affects the decision of which other items should be chosen alongside it.\n', 'In this work, we propose a sequence-to-sequence model for ranking called seq2slate.', 'At each step, the model predicts the next item to place on the slate given the items already chosen.', 'The recurrent nature of the model allows complex dependencies between items to be captured directly in a flexible and scalable way.', 'We show how to learn the model end-to-end from weak supervision in the form of easily obtained click-through data.', 'We further demonstrate the usefulness of our approach in experiments on standard ranking benchmarks as well as in a real-world recommendation system.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['While momentum-based methods, in conjunction with the stochastic gradient descent, are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods.', 'In practice, the momentum parameter is often chosen in a heuristic fashion with little theoretical guidance.', 'In this work, we use the framework of algorithmic stability to provide an upper-bound on the generalization error for the class of strongly convex loss functions, under mild technical assumptions.', 'Our bound decays to zero inversely with the size of the training set, and increases as the momentum parameter is increased.', 'We also develop an upper-bound on the expected true risk, in terms of the number of training steps, the size of the training set, and the momentum parameter.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Imitation learning from demonstrations usually relies on learning a policy from trajectories of optimal states and actions.', 'However, in real life expert demonstrations, often the action information is missing and only state trajectories are available.', 'We present a model-based imitation learning method that can learn environment-specific optimal actions only from expert state trajectories.', 'Our proposed method starts with a model-free reinforcement learning algorithm with a heuristic reward signal to sample environment dynamics, which is then used to train the state-transition probability.', 'Subsequently, we learn the optimal actions from expert state trajectories by supervised learning, while back-propagating the error gradients through the modeled environment dynamics.', 'Experimental evaluations show that our proposed method successfully achieves performance similar to (state, action) trajectory-based traditional imitation learning methods even in the absence of action information, with much fewer iterations compared to conventional model-free reinforcement learning methods.', 'We also demonstrate that our method can learn to act from only video demonstrations of expert agent for simple games and can learn to achieve desired performance in less number of iterations.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Recent research has proposed the lottery ticket hypothesis, suggesting that for a deep neural network, there exist trainable sub-networks performing equally or better than the original model with commensurate training steps.', 'While this discovery is insightful, finding proper sub-networks requires iterative training and pruning.', 'The high cost incurred limits the applications of the lottery ticket hypothesis.', 'We show there exists a subset of the aforementioned sub-networks that converge significantly faster during the training process and thus can mitigate the cost issue.', 'We conduct extensive experiments to show such sub-networks consistently exist across various model structures for a restrictive setting of hyperparameters (e.g., carefully selected learning rate, pruning ratio, and model capacity). ', 'As a practical application of our findings, we demonstrate that such sub-networks can help in cutting down the total time of adversarial training, a standard approach to improve robustness, by up to 49% on CIFAR-10 to achieve the state-of-the-art robustness.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Disentangled representations, where the higher level data generative factors are reflected in disjoint latent dimensions, offer several benefits such as ease of deriving invariant representations, transferability to other tasks, interpretability, etc.', 'We consider the problem of unsupervised learning of disentangled representations from large pool of unlabeled observations, and propose a variational inference based approach to infer disentangled latent factors.', 'We introduce a regularizer on the expectation of the approximate posterior over observed data that encourages the disentanglement.', "We also propose a new disentanglement metric which is better aligned with the qualitative disentanglement observed in the decoder's output.", 'We empirically observe significant improvement over existing methods in terms of both disentanglement and data likelihood (reconstruction quality). \n\n'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['In multiagent systems (MASs), each agent makes individual decisions but all of them contribute globally to the system evolution.', "Learning in MASs is difficult since each agent's selection of actions must take place in the presence of other co-learning agents.", 'Moreover, the environmental stochasticity and uncertainties increase exponentially with the increase in the number of agents.', 'Previous works borrow various multiagent coordination mechanisms into deep learning architecture to facilitate multiagent coordination.', 'However, none of them explicitly consider action semantics between agents that different actions have different influences on other agents.', 'In this paper, we propose a novel network architecture, named Action Semantics Network (ASN), that explicitly represents such action semantics between agents.', "ASN characterizes different actions' influence on other agents using neural networks based on the action semantics between them.", 'ASN can be easily combined with existing deep reinforcement learning (DRL) algorithms to boost their performance.', 'Experimental results on StarCraft II micromanagement and Neural MMO show ASN significantly improves the performance of state-of-the-art DRL approaches compared with several network architectures.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Spiking neural networks are being investigated both as biologically plausible models of neural computation and also as a potentially more efficient type of neural network.', 'While convolutional spiking neural networks have been demonstrated to achieve near state-of-the-art performance, only one solution has been proposed to convert gated recurrent neural networks, so far.\n', 'Recurrent neural networks in the form of networks of gating memory cells have been central in state-of-the-art solutions in problem domains that involve sequence recognition or generation.', 'Here, we design an analog gated LSTM cell where its neurons can be substituted for efficient stochastic spiking neurons.', 'These adaptive spiking neurons implement an adaptive form of sigma-delta coding to convert internally computed analog activation values to spike-trains.', 'For such neurons, we approximate the effective activation function, which resembles a sigmoid.', 'We show how analog neurons with such activation functions can be used to create an analog LSTM cell; networks of these cells can then be trained with standard backpropagation.', 'We train these LSTM networks on a noisy and noiseless version of the original sequence prediction task from Hochreiter & Schmidhuber (1997), and also on a noisy and noiseless version of a classical working memory reinforcement learning task, the T-Maze.', 'Substituting the analog neurons for corresponding adaptive spiking neurons, we then show that almost all resulting spiking neural network equivalents correctly compute the original tasks.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['CNNs are widely successful in recognizing human actions in videos, albeit with a great cost of computation.', 'This cost is significantly higher in the case of long-range actions, where a video can span up to a few minutes, on average.', 'The goal of this paper is to reduce the computational cost of these CNNs, without sacrificing their performance.', 'We propose VideoEpitoma, a neural network architecture comprising two modules: a timestamp selector and a video classifier.', 'Given a long-range video of thousands of timesteps, the selector learns to choose only a few but most representative timesteps for the video.', 'This selector resides on top of a lightweight CNN such as MobileNet and uses a novel gating module to take a binary decision: consider or discard a video timestep.', 'This decision is conditioned on both the timestep-level feature and the video-level consensus.', 'A heavyweight CNN model such as I3D takes the selected frames as input and performs video classification.', 'Using off-the-shelf video classifiers, VideoEpitoma reduces the computation by up to 50\\% without compromising the accuracy.', 'In addition, we show that if trained end-to-end, the selector learns to make better choices to the benefit of the classifier, despite the selector and the classifier residing on two different CNNs.', 'Finally, we report state-of-the-art results on two datasets for long-range action recognition: Charades and Breakfast Actions, with much-reduced computation.', 'In particular, we match the accuracy of I3D by using less than half of the computation.\n\n'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Human annotation for syntactic parsing is expensive, and large resources are available only for a fraction of languages.', 'A question we ask is whether one can leverage abundant unlabeled texts to improve syntactic parsers, beyond just using the texts to obtain more generalisable lexical features (i.e. beyond word embeddings).', 'To this end, we propose a novel latent-variable generative model for semi-supervised syntactic dependency parsing.', 'As exact inference is intractable, we introduce a differentiable relaxation to obtain approximate samples and compute gradients with respect to the parser parameters.', 'Our method (Differentiable Perturb-and-Parse) relies on differentiable dynamic programming over stochastically perturbed edge scores.', 'We demonstrate effectiveness of our approach with experiments on English, French and Swedish.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['DeConvNet, Guided BackProp, LRP, were invented to better understand deep neural networks.', 'We show that these methods do not produce the theoretically correct explanation for a linear model.', 'Yet they are used on multi-layer networks with millions of parameters.', 'This is a cause for concern since linear models are simple neural networks.', 'We argue that explanation methods for neural nets should work reliably in the limit of simplicity, the linear models.', 'Based on our analysis of linear models we propose a generalization that yields two explanation techniques (PatternNet and PatternAttribution) that are theoretically sound for linear models and produce improved explanations for deep networks.\n'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Graph neural networks have shown promising results on representing and analyzing diverse graph-structured data such as social, citation, and protein interaction networks.', 'Existing approaches commonly suffer from the oversmoothing issue, regardless of whether policies are edge-based or node-based for neighborhood aggregation.', 'Most methods also focus on transductive scenarios for fixed graphs, leading to poor generalization performance for unseen graphs.', 'To address these issues, we propose a new graph neural network model that considers both edge-based neighborhood relationships and node-based entity features, i.e. Graph Entities with Step Mixture via random walk (GESM).', 'GESM employs a mixture of various steps through random walk to alleviate the oversmoothing problem and attention to use node information explicitly.', 'These two mechanisms allow for a weighted neighborhood aggregation which considers the properties of entities and relations.', 'With intensive experiments, we show that the proposed GESM achieves state-of-the-art or comparable performances on four benchmark graph datasets comprising transductive and inductive learning tasks.', 'Furthermore, we empirically demonstrate the significance of considering global information.', 'The source code will be publicly available in the near future.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Basis pursuit is a compressed sensing optimization in which the l1-norm is minimized subject to model error constraints.', 'Here we use a deep neural network prior instead of l1-regularization.', 'Using known noise statistics, we jointly learn the prior and reconstruct images without access to ground-truth data.', 'During training, we use alternating minimization across an unrolled iterative network and jointly solve for the neural network weights and training set image reconstructions.', 'At inference, we fix the weights and pass the measurements through the network.', 'We compare reconstruction performance between unsupervised and supervised (i.e. with ground-truth) methods.', 'We hypothesize this technique could be used to learn reconstruction when ground-truth data are unavailable, such as in high-resolution dynamic MRI.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Deep learning approaches usually require a large amount of labeled data to generalize.', 'However, humans can learn a new concept only by a few samples.', 'One of the high cogntition human capablities is to learn several concepts at the same time.', 'In this paper, we address the task of classifying multiple objects by seeing only a few samples from each category.', "To the best of authors' knowledge, there is no dataset specially designed for few-shot multiclass classification.", 'We design a task of mutli-object few class classification and an environment for easy creating controllable datasets for this task.', 'We demonstrate that the proposed dataset is sound using a method which is an extension of prototypical networks.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['We present a new approach to defining a sequence loss function to train a summarizer by using a secondary encoder-decoder as a loss function, alleviating a shortcoming of word level training for sequence outputs.', 'The technique is based on the intuition that if a summary is a good one, it should contain the most essential information from the original article, and therefore should itself be a good input sequence, in lieu of the original, from which a summary can be generated.', 'We present experimental results where we apply this additional loss function to a general abstractive summarizer on a news summarization dataset.', 'The result is an improvement in the ROUGE metric and an especially large improvement in human evaluations, suggesting enhanced performance that is competitive with specialized state-of-the-art models.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Existing unsupervised video-to-video translation methods fail to produce translated videos which are frame-wise realistic, semantic information preserving and video-level consistent.', 'In this work, we propose a novel unsupervised video-to-video translation model.', 'Our model decomposes the style and the content, uses specialized encoder-decoder structure and propagates the inter-frame information through bidirectional recurrent neural network (RNN) units.', 'The style-content decomposition mechanism enables us to achieve long-term style-consistent video translation results as well as provides us with a good interface for modality flexible translation.', 'In addition, by changing the input frames and style codes incorporated in our translation, we propose a video interpolation loss, which captures temporal information within the sequence to train our building blocks in a self-supervised manner.', 'Our model can produce photo-realistic, spatio-temporal consistent translated videos in a multimodal way.', 'Subjective and objective experimental results validate the superiority of our model over the existing methods.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Providing transparency of AI planning systems is crucial for their success in practical applications.', 'In order to create a transparent system, a user must be able to query it for explanations about its outputs.', 'We argue that a key underlying principle for this is the use of causality within a planning model, and that argumentation frameworks provide an intuitive representation of such causality.', 'In this paper, we discuss how argumentation can aid in extracting causalities in plans and models, and how they can create explanations from them.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Point clouds, as a form of Lagrangian representation, allow for powerful and flexible applications in a large number of computational disciplines.', 'We propose a novel deep-learning method to learn stable and temporally coherent feature spaces for points clouds that change over time.', 'We identify a set of inherent problems with these approaches: without knowledge of the time dimension, the inferred solutions can exhibit strong flickering, and easy solutions to suppress this flickering can result in undesirable local minima that manifest themselves as halo structures.', 'We propose a novel temporal loss function that takes into account higher time derivatives of the point positions, and encourages mingling, i.e., to prevent the aforementioned halos.', 'We combine these techniques in a super-resolution method with a truncation approach to flexibly adapt the size of the generated positions.', 'We show that our method works for large, deforming point sets from different sources to demonstrate the flexibility of our approach.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['We study the problem of generating adversarial examples in a black-box setting in which only loss-oracle access to a model is available.', 'We introduce a framework that conceptually unifies much of the existing work on black-box attacks, and demonstrate that the current state-of-the-art methods are optimal in a natural sense.', 'Despite this optimality, we show how to improve black-box attacks by bringing a new element into the problem: gradient priors.', 'We give a bandit optimization-based algorithm that allows us to seamlessly integrate any such priors, and we explicitly identify and incorporate two examples.', 'The resulting methods use two to four times fewer queries and fail two to five times less than the current state-of-the-art.', 'The code for reproducing our work is available at https://git.io/fAjOJ.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Collecting high-quality, large scale datasets typically requires significant resources.', 'The aim of the present work is to improve the label efficiency of large neural networks operating on audio data through multitask learning with self-supervised tasks on unlabeled data.', 'To this end, we trained an end-to-end audio feature extractor based on WaveNet that feeds into simple, yet versatile task-specific neural networks.', 'We describe three self-supervised learning tasks that can operate on any large, unlabeled audio corpus.', 'We demonstrate that, in a scenario with limited labeled training data, one can significantly improve the performance of a supervised classification task by simultaneously training it with these additional self-supervised tasks.', 'We show that one can improve performance on a diverse sound events classification task by nearly 6\\% when jointly trained with up to three distinct self-supervised tasks.', 'This improvement scales with the number of additional auxiliary tasks as well as the amount of unsupervised data.', 'We also show that incorporating data augmentation into our multitask setting leads to even further gains in performance.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Information need of humans is essentially multimodal in nature, enabling maximum exploitation of situated context.', 'We introduce a dataset for sequential procedural (how-to) text generation from images in cooking domain.', 'The dataset consists of 16,441 cooking recipes with 160,479 photos associated with different steps.', 'We setup a baseline motivated by the best performing model in terms of human evaluation for the Visual Story Telling (ViST) task.', 'In addition, we introduce two models to incorporate high level structure learnt by a Finite State Machine (FSM) in neural sequential generation process by: (1) Scaffolding Structure in Decoder (SSiD) (2) Scaffolding Structure in Loss (SSiL).', 'These models show an improvement in empirical as well as human evaluation.', 'Our best performing model (SSiL) achieves a METEOR score of 0.31, which is an improvement of 0.6 over the baseline model.', 'We also conducted human evaluation of the generated grounded recipes, which reveal that 61% found that our proposed (SSiL) model is better than the baseline model in terms of overall recipes, and 72.5% preferred our model in terms of coherence and structure.', 'We also discuss analysis of the output highlighting key important NLP issues for prospective directions.\n'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Implicit models, which allow for the generation of samples but not for point-wise evaluation of probabilities, are omnipresent in real-world problems tackled by machine learning and a hot topic of current research.', 'Some examples include data simulators that are widely used in engineering and scientific research, generative adversarial networks (GANs) for image synthesis, and hot-off-the-press approximate inference techniques relying on implicit distributions.', 'The majority of existing approaches to learning implicit models rely on approximating the intractable distribution or optimisation objective for gradient-based optimisation, which is liable to produce inaccurate updates and thus poor models.', 'This paper alleviates the need for such approximations by proposing the \\emph{Stein gradient estimator}, which directly estimates the score function of the implicitly defined distribution.', 'The efficacy of the proposed estimator is empirically demonstrated by examples that include meta-learning for approximate inference and entropy regularised GANs that provide improved sample diversity.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Noise injection is a fundamental tool for data augmentation, and yet there is no widely accepted procedure to incorporate it with learning frameworks.', 'This study analyzes the effects of adding or applying different noise models of varying magnitudes to Convolutional Neural Network (CNN) architectures.', 'Noise models that are distributed with different density functions are given common magnitude levels via Structural Similarity (SSIM) metric in order to create an appropriate ground for comparison.', 'The basic results are conforming with the most of the common notions in machine learning, and also introduces some novel heuristics and recommendations on noise injection.', 'The new approaches will provide better understanding on optimal learning procedures for image classification.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['State-of-the-art Unsupervised Domain Adaptation (UDA) methods learn transferable features by minimizing the feature distribution discrepancy between the source and target domains.', 'Different from these methods which do not model the feature distributions explicitly, in this paper, we explore explicit feature distribution modeling for UDA.', 'In particular, we propose Distribution Matching Prototypical Network (DMPN) to model the deep features from each domain as Gaussian mixture distributions.', 'With explicit feature distribution modeling, we can easily measure the discrepancy between the two domains.', 'In DMPN, we propose two new domain discrepancy losses with probabilistic interpretations.', 'The first one minimizes the distances between the corresponding Gaussian component means of the source and target data.', 'The second one minimizes the pseudo negative log likelihood of generating the target features from source feature distribution.', 'To learn both discriminative and domain invariant features, DMPN is trained by minimizing the classification loss on the labeled source data and the domain discrepancy losses together.', 'Extensive experiments are conducted over two UDA tasks.', 'Our approach yields a large margin in the Digits Image transfer task over state-of-the-art approaches.', 'More remarkably, DMPN obtains a mean accuracy of 81.4% on VisDA 2017 dataset.', 'The hyper-parameter sensitivity analysis shows that our approach is robust w.r.t hyper-parameter changes.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Efficiently learning to solve tasks in complex environments is a key challenge for reinforcement learning (RL) agents.', ' We propose to decompose a complex environment using a task-agnostic world graphs, an abstraction that accelerates learning by enabling agents to focus exploration on a subspace of the environment.The nodes of a world graph are important waypoint states and edges represent feasible traversals between them', '. Our framework has two learning phases', ': 1) identifying world graph nodes and edges by training a binary recurrent variational auto-encoder (VAE) on trajectory data and', '2) a hierarchical RL framework that leverages structural and connectivity knowledge from the learned world graph to bias exploration towards task-relevant waypoints and regions.', 'We show that our approach significantly accelerates RL on a suite of challenging 2D grid world tasks: compared to baselines, world graph integration doubles achieved rewards on simpler tasks, e.g. MultiGoal, and manages to solve more challenging tasks, e.g. Door-Key, where baselines fail.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['We introduce the notion of property signatures, a representation for programs and\n', 'program specifications meant for consumption by machine learning algorithms.\n', 'Given a function with input type τ_in and output type τ_out, a property is a function\n', 'of type: (τ_in, τ_out) → Bool that (informally) describes some simple property\n', 'of the function under consideration.', 'For instance, if τ_in and τ_out are both lists\n', 'of the same type, one property might ask ‘is the input list the same length as the\n', 'output list?’.', 'If we have a list of such properties, we can evaluate them all for our\n', 'function to get a list of outputs that we will call the property signature.', 'Crucially,\n', 'we can ‘guess’ the property signature for a function given only a set of input/output\n', 'pairs meant to specify that function.', 'We discuss several potential applications of\n', 'property signatures and show experimentally that they can be used to improve\n', 'over a baseline synthesizer so that it emits twice as many programs in less than\n', 'one-tenth of the time.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Social dilemmas are situations where individuals face a temptation to increase their payoffs at a cost to total welfare.', 'Building artificially intelligent agents that achieve good outcomes in these situations is important because many real world interactions include a tension between selfish interests and the welfare of others.', 'We show how to modify modern reinforcement learning methods to construct agents that act in ways that are simple to understand, nice (begin by cooperating), provokable (try to avoid being exploited), and forgiving (try to return to mutual cooperation).', 'We show both theoretically and experimentally that such agents can maintain cooperation in Markov social dilemmas.', 'Our construction does not require training methods beyond a modification of self-play, thus if an environment is such that good strategies can be constructed in the zero-sum case (eg. Atari) then we can construct agents that solve social dilemmas in this environment.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['The reparameterization trick has become one of the most useful tools in the field of variational inference.', 'However, the reparameterization trick is based on the standardization transformation which restricts the scope of application of this method to distributions that have tractable inverse cumulative distribution functions or are expressible as deterministic transformations of such distributions.', 'In this paper, we generalized the reparameterization trick by allowing a general transformation.', 'Unlike other similar works, we develop the generalized transformation-based gradient model formally and rigorously.', 'We discover that the proposed model is a special case of control variate indicating that the proposed model can combine the advantages of CV and generalized reparameterization.', 'Based on the proposed gradient model, we propose a new polynomial-based gradient estimator which has better theoretical performance than the reparameterization trick under certain condition and can be applied to a larger class of variational distributions.', 'In studies of synthetic and real data, we show that our proposed gradient estimator has a significantly lower gradient variance than other state-of-the-art methods thus enabling a faster inference procedure.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['The fault diagnosis in a modern communication system is traditionally supposed to be difficult, or even impractical for a purely data-driven machine learning approach, for it is a humanmade system of intensive knowledge.', 'A few labeled raw packet streams extracted from fault archive can hardly be sufficient to deduce the intricate logic of underlying protocols.', 'In this paper, we supplement these limited samples with two inexhaustible data sources: the unlabeled records probed from a system in service, and the labeled data simulated in an emulation environment.', 'To transfer their inherent knowledge to the target domain, we construct a directed information flow graph, whose nodes are neural network components consisting of two generators, three discriminators and one classifier, and whose every forward path represents a pair of adversarial optimization goals, in accord with the semi-supervised and transfer learning demands.', 'The multi-headed network can be trained in an alternative approach, at each iteration of which we select one target to update the weights along the path upstream, and refresh the residual layer-wisely to all outputs downstream.', 'The actual results show that it can achieve comparable accuracy on classifying Transmission Control Protocol (TCP) streams without deliberate expert features.', 'The solution has relieved operation engineers from massive works of understanding and maintaining rules, and provided a quick solution independent of specific protocols.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Our work addresses two important issues with recurrent neural networks: (1) they are over-parameterized, and (2) the recurrent weight matrix is ill-conditioned.', 'The former increases the sample complexity of learning and the training time.', 'The latter causes the vanishing and exploding gradient problem.', 'We present a flexible recurrent neural network model called Kronecker Recurrent Units (KRU).', 'KRU achieves parameter efficiency in RNNs through a Kronecker factored recurrent matrix.', 'It overcomes the ill-conditioning of the recurrent matrix by enforcing soft unitary constraints on the factors.', 'Thanks to the small dimensionality of the factors, maintaining these constraints is computationally efficient.', 'Our experimental results on seven standard data-sets reveal that KRU can reduce the number of parameters by three orders of magnitude in the recurrent weight matrix compared to the existing recurrent models, without trading the statistical performance.', 'These results in particular show that while there are advantages in having a high dimensional recurrent space, the capacity of the recurrent part of the model can be dramatically reduced.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['This paper studies the undesired phenomena of over-sensitivity of representations learned by deep networks to semantically-irrelevant changes in data.', 'We identify a cause for this shortcoming in the classical Variational Auto-encoder (VAE) objective, the evidence lower bound (ELBO).', 'We show that the ELBO fails to control the behaviour of the encoder out of the support of the empirical data distribution and this behaviour of the VAE can lead to extreme errors in the learned representation.', 'This is a key hurdle in the effective use of representations for data-efficient learning and transfer.', 'To address this problem, we propose to augment the data with specifications that enforce insensitivity of the representation with respect to families of transformations.', 'To incorporate these specifications, we propose a regularization method that is based on a selection mechanism that creates a fictive data point by explicitly perturbing an observed true data point.', 'For certain choices of parameters, our formulation naturally leads to the minimization of the entropy regularized Wasserstein distance between representations.', 'We illustrate our approach on standard datasets and experimentally show that significant improvements in the downstream adversarial accuracy can be achieved by learning robust representations completely in an unsupervised manner, without a reference to a particular downstream task and without a costly supervised adversarial training procedure. \n'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['We propose a novel score-based approach to learning a directed acyclic graph (DAG) from observational data.', 'We adapt a recently proposed continuous constrained optimization formulation to allow for nonlinear relationships between variables using neural networks.', 'This extension allows to model complex interactions while being more global in its search compared to other greedy approaches.', 'In addition to comparing our method to existing continuous optimization methods, we provide missing empirical comparisons to nonlinear greedy search methods.', 'On both synthetic and real-world data sets, this new method outperforms current continuous methods on most tasks while being competitive with existing greedy search methods on important metrics for causal inference.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['We study the problem of designing provably optimal adversarial noise algorithms that induce misclassification in settings where a learner aggregates decisions from multiple classifiers.', 'Given the demonstrated vulnerability of state-of-the-art models to adversarial examples, recent efforts within the field of robust machine learning have focused on the use of ensemble classifiers as a way of boosting the robustness of individual models.', 'In this paper, we design provably optimal attacks against a set of classifiers.', 'We demonstrate how this problem can be framed as finding strategies at equilibrium in a two player, zero sum game between a learner and an adversary and consequently illustrate the need for randomization in adversarial attacks.', 'The main technical challenge we consider is the design of best response oracles that can be implemented in a Multiplicative Weight Updates framework to find equilibrium strategies in the zero-sum game.', 'We develop a series of scalable noise generation algorithms for deep neural networks, and show that it outperforms state-of-the-art attacks on various image classification tasks.', 'Although there are generally no guarantees for deep learning, we show this is a well-principled approach in that it is provably optimal for linear classifiers.', 'The main insight is a geometric characterization of the decision space that reduces the problem of designing best response oracles to minimizing a quadratic function over a set of convex polytopes.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Multiagent systems where the agents interact among themselves and with an stochastic environment can be formalized as stochastic games.', 'We study a subclass of these games, named Markov potential games (MPGs), that appear often in economic and engineering applications when the agents share some common resource.', 'We consider MPGs with continuous state-action variables, coupled constraints and nonconvex rewards.', 'Previous analysis followed a variational approach that is only valid for very simple cases (convex rewards, invertible dynamics, and no coupled constraints); or considered deterministic dynamics and provided open-loop (OL) analysis, studying strategies that consist in predefined action sequences, which are not optimal for stochastic environments.', 'We present a closed-loop (CL) analysis for MPGs and consider parametric policies that depend on the current state and where agents adapt to stochastic transitions.', 'We provide easily verifiable, sufficient and necessary conditions for a stochastic game to be an MPG, even for complex parametric functions (e.g., deep neural networks); and show that a closed-loop Nash equilibrium (NE) can be found (or at least approximated) by solving a related optimal control problem (OCP).', 'This is useful since solving an OCP---which is a single-objective problem---is usually much simpler than solving the original set of coupled OCPs that form the game---which is a multiobjective control problem.', 'This is a considerable improvement over the previously standard approach for the CL analysis of MPGs, which gives no approximate solution if no NE belongs to the chosen parametric family, and which is practical only for simple parametric forms.', 'We illustrate the theoretical contributions with an example by applying our approach to a noncooperative communications engineering game.', 'We then solve the game with a deep reinforcement learning algorithm that learns policies that closely approximates an exact variational NE of the game.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['We make the following striking observation: fully convolutional VAE models trained on 32x32 ImageNet can generalize well, not just to 64x64 but also to far larger photographs, with no changes to the model.', "We use this property, applying fully convolutional models to lossless compression, demonstrating a method to scale the VAE-based 'Bits-Back with ANS' algorithm for lossless compression to large color photographs, and achieving state of the art for compression of full size ImageNet images.", 'We release Craystack, an open source library for convenient prototyping of lossless compression using probabilistic models, along with full implementations of all of our compression results.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: [' State of the art computer vision models have been shown to be vulnerable to small adversarial perturbations of the input.', 'In other words, most images in the data distribution are both correctly classified by the model and are very close to a visually similar misclassified image.', 'Despite substantial research interest, the cause of the phenomenon is still poorly understood and remains unsolved.', 'We hypothesize that this counter intuitive behavior is a naturally occurring result of the high dimensional geometry of the data manifold.', 'As a first step towards exploring this hypothesis, we study a simple synthetic dataset of classifying between two concentric high dimensional spheres.', 'For this dataset we show a fundamental tradeoff between the amount of test error and the average distance to nearest error.', 'In particular, we prove that any model which misclassifies a small constant fraction of a sphere will be vulnerable to adversarial perturbations of size $O(1/\\sqrt{d})$.', 'Surprisingly, when we train several different architectures on this dataset, all of their error sets naturally approach this theoretical bound.', 'As a result of the theory, the vulnerability of neural networks to small adversarial perturbations is a logical consequence of the amount of test error observed.', 'We hope that our theoretical analysis of this very simple case will point the way forward to explore how the geometry of complex real-world data sets leads to adversarial examples.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['It has been established that diverse behaviors spanning the controllable subspace of a Markov decision process can be trained by rewarding a policy for being distinguishable from other policies.', 'However, one limitation of this formulation is the difficulty to generalize beyond the finite set of behaviors being explicitly learned, as may be needed in subsequent tasks.', 'Successor features provide an appealing solution to this generalization problem, but require defining the reward function as linear in some grounded feature space.', "In this paper, we show that these two techniques can be combined, and that each method solves the other's primary limitation.", 'To do so we introduce Variational Intrinsic Successor FeatuRes (VISR), a novel algorithm which learns controllable features that can be leveraged to provide enhanced generalization and fast task inference through the successor features framework.', 'We empirically validate VISR on the full Atari suite, in a novel setup wherein the rewards are only exposed briefly after a long unsupervised phase.', 'Achieving human-level performance on 12 games and beating all baselines, we believe VISR represents a step towards agents that rapidly learn from limited feedback.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Predicting structured outputs such as semantic segmentation relies on expensive per-pixel annotations to learn strong supervised models like convolutional neural networks.', 'However, these models trained on one data domain may not generalize well to other domains unequipped with annotations for model finetuning.', 'To avoid the labor-intensive process of annotation, we develop a domain adaptation method to adapt the source data to the unlabeled target domain.', 'To this end, we propose to learn discriminative feature representations of patches based on label histograms in the source domain, through the construction of a disentangled space.', 'With such representations as guidance, we then use an adversarial learning scheme to push the feature representations in target patches to the closer distributions in source ones.', 'In addition, we show that our framework can integrate a global alignment process with the proposed patch-level alignment and achieve state-of-the-art performance on semantic segmentation.', 'Extensive ablation studies and experiments are conducted on numerous benchmark datasets with various settings, such as synthetic-to-real and cross-city scenarios.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Many machine learning algorithms are vulnerable to almost imperceptible perturbations of their inputs.', 'So far it was unclear how much risk adversarial perturbations carry for the safety of real-world machine learning applications because most methods used to generate such perturbations rely either on detailed model information (gradient-based attacks) or on confidence scores such as class probabilities (score-based attacks), neither of which are available in most real-world scenarios.', 'In many such cases one currently needs to retreat to transfer-based attacks which rely on cumbersome substitute models, need access to the training data and can be defended against.', 'Here we emphasise the importance of attacks which solely rely on the final model decision.', 'Such decision-based attacks are (1) applicable to real-world black-box models such as autonomous cars, (2) need less knowledge and are easier to apply than transfer-based attacks and (3) are more robust to simple defences than gradient- or score-based attacks.', 'Previous attacks in this category were limited to simple models or simple datasets.', 'Here we introduce the Boundary Attack, a decision-based attack that starts from a large adversarial perturbation and then seeks to reduce the perturbation while staying adversarial.', 'The attack is conceptually simple, requires close to no hyperparameter tuning, does not rely on substitute models and is competitive with the best gradient-based attacks in standard computer vision tasks like ImageNet.', 'We apply the attack on two black-box algorithms from Clarifai.com.', 'The Boundary Attack in particular and the class of decision-based attacks in general open new avenues to study the robustness of machine learning models and raise new questions regarding the safety of deployed machine learning systems.', 'An implementation of the attack is available as part of Foolbox (https://github.com/bethgelab/foolbox).'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['We demonstrate that it is possible to train large recurrent language models with user-level differential privacy guarantees with only a negligible cost in predictive accuracy. ', 'Our work builds on recent advances in the training of deep networks on user-partitioned data and privacy accounting for stochastic gradient descent.', 'In particular, we add user-level privacy protection to the federated averaging algorithm, which makes large step updates from user-level data.', 'Our work demonstrates that given a dataset with a sufficiently large number of users (a requirement easily met by even small internet-scale datasets), achieving differential privacy comes at the cost of increased computation, rather than in decreased utility as in most prior work.', 'We find that our private LSTM language models are quantitatively and qualitatively similar to un-noised models when trained on a large dataset.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Convolutional neural networks (CNNs) are commonly trained using a fixed spatial image size predetermined for a given model.', 'Although trained on images of a specific size, it is well established that CNNs can be used to evaluate a wide range of image sizes at test time, by adjusting the size of intermediate feature maps. \n', 'In this work, we describe and evaluate a novel mixed-size training regime that mixes several image sizes at training time.', 'We demonstrate that models trained using our method are more resilient to image size changes and generalize well even on small images.', 'This allows faster inference by using smaller images at test time.', 'For instance, we receive a 76.43% top-1 accuracy using ResNet50 with an image size of 160, which matches the accuracy of the baseline model with 2x fewer computations.\n', 'Furthermore, for a given image size used at test time, we show this method can be exploited either to accelerate training or the final test accuracy.', 'For example, we are able to reach a 79.27% accuracy with a model evaluated at a 288 spatial size for a relative improvement of 14% over the baseline.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['We propose a simple technique for encouraging generative RNNs to plan ahead.', "We train a ``backward'' recurrent network to generate a given sequence in reverse order, and we encourage states of the forward model to predict cotemporal states of the backward model.", 'The backward network is used only during training, and plays no role during sampling or inference.', 'We hypothesize that our approach eases modeling of long-term dependencies by implicitly forcing the forward states to hold information about the longer-term future (as contained in the backward states).', 'We show empirically that our approach achieves 9% relative improvement for a speech recognition task, and achieves significant improvement on a COCO caption generation task.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Deep generative models seek to recover the process with which the observed data was generated.', 'They may be used to synthesize new samples or to subsequently extract representations.', 'Successful approaches in the domain of images are driven by several core inductive biases.', 'However, a bias to account for the compositional way in which humans structure a visual scene in terms of objects has frequently been overlooked.', 'In this work we propose to structure the generator of a GAN to consider objects and their relations explicitly, and generate images by means of composition.', 'This provides a way to efficiently learn a more accurate generative model of real-world images, and serves as an initial step towards learning corresponding object representations.', 'We evaluate our approach on several multi-object image datasets, and find that the generator learns to identify and disentangle information corresponding to different objects at a representational level.', 'A human study reveals that the resulting generative model is better at generating images that are more faithful to the reference distribution.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Current literature in machine learning holds that unaligned, self-interested agents do not learn to use an emergent communication channel.', 'We introduce a new sender-receiver game to study emergent communication for this spectrum of partially-competitive scenarios and put special care into evaluation.', 'We find that communication can indeed emerge in partially-competitive scenarios, and we discover three things that are tied to improving it.', 'First, that selfish communication is proportional to cooperation, and it naturally occurs for situations that are more cooperative than competitive.', 'Second, that stability and performance are improved by using LOLA (Foerster et al, 2018), especially in more competitive scenarios.', 'And third, that discrete protocols lend themselves better to learning cooperative communication than continuous ones.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Deep neural networks (DNNs) typically have enough capacity to fit random data by brute force even when conventional data-dependent regularizations focusing on the geometry of the features are imposed.', 'We find out that the reason for this is the inconsistency between the enforced geometry and the standard softmax cross entropy loss.', 'To resolve this, we propose a new framework for data-dependent DNN regularization, the Geometrically-Regularized-Self-Validating neural Networks (GRSVNet).', 'During training, the geometry enforced on one batch of features is simultaneously validated on a separate batch using a validation loss consistent with the geometry.', 'We study a particular case of GRSVNet, the Orthogonal-Low-rank Embedding (OLE)-GRSVNet, which is capable of producing highly discriminative features residing in orthogonal low-rank subspaces.', 'Numerical experiments show that OLE-GRSVNet outperforms DNNs with conventional regularization when trained on real data.', 'More importantly, unlike conventional DNNs, OLE-GRSVNet refuses to memorize random data or random labels, suggesting it only learns intrinsic patterns by reducing the memorizing capacity of the baseline DNN.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['End-to-end automatic speech recognition (ASR) commonly transcribes audio signals into sequences of characters while its performance is evaluated by measuring the word-error rate (WER).', 'This suggests that predicting sequences of words directly may be helpful instead.', 'However, training with word-level supervision can be more difficult due to the sparsity of examples per label class.', 'In this paper we analyze an end-to-end ASR model that combines a word-and-character representation in a multi-task learning (MTL) framework.', 'We show that it improves on the WER and study how the word-level model can benefit from character-level supervision by analyzing the learned inductive preference bias of each model component empirically.', 'We find that by adding character-level supervision, the MTL model interpolates between recognizing more frequent words (preferred by the word-level model) and shorter words (preferred by the character-level model).'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Discretizing floating-point vectors is a fundamental step of modern indexing methods.', 'State-of-the-art techniques learn parameters of the quantizers on training data for optimal performance, thus adapting quantizers to the data.', 'In this work, we propose to reverse this paradigm and adapt the data to the quantizer: we train a neural net whose last layers form a fixed parameter-free quantizer, such as pre-defined points of a sphere.', 'As a proxy objective, we design and train a neural network that favors uniformity in the spherical latent space, while preserving the neighborhood structure after the mapping. ', 'For this purpose, we propose a new regularizer derived from the Kozachenko-Leonenko differential entropy estimator and combine it with a locality-aware triplet loss. \n', 'Experiments show that our end-to-end approach outperforms most learned quantization methods, and is competitive with the state of the art on widely adopted benchmarks.', 'Further more, we show that training without the quantization step results in almost no difference in accuracy, but yields a generic catalyser that can be applied with any subsequent quantization technique.\n'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Temporal difference (TD) learning is a popular algorithm for policy evaluation in reinforcement learning, but the vanilla TD can substantially suffer from the inherent optimization variance.', 'A variance reduced TD (VRTD) algorithm was proposed by Korda and La (2015), which applies the variance reduction technique directly to the online TD learning with Markovian samples.', 'In this work, we first point out the technical errors in the analysis of VRTD in Korda and La (2015), and then provide a mathematically solid analysis of the non-asymptotic convergence of VRTD and its variance reduction performance.', 'We show that VRTD is guaranteed to converge to a neighborhood of the fixed-point solution of TD at a linear convergence rate.', 'Furthermore, the variance error (for both i.i.d. and Markovian sampling) and the bias error (for Markovian sampling) of VRTD are significantly reduced by the batch size of variance reduction in comparison to those of vanilla TD.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['We tackle unsupervised domain adaptation by accounting for the fact that different domains may need to be processed differently to arrive to a common feature representation effective for recognition.', 'To this end, we introduce a deep learning framework where each domain undergoes a different sequence of operations, allowing some, possibly more complex, domains to go through more computations than others.\n', 'This contrasts with state-of-the-art domain adaptation techniques that force all domains to be processed with the same series of operations, even when using multi-stream architectures whose parameters are not shared.\n', 'As evidenced by our experiments, the greater flexibility of our method translates to higher accuracy.', 'Furthermore, it allows us to handle any number of domains simultaneously.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['The practical usage of reinforcement learning agents is often bottlenecked by the duration of training time.', 'To accelerate training, practitioners often turn to distributed reinforcement learning architectures to parallelize and accelerate the training process.', 'However, modern methods for scalable reinforcement learning (RL) often tradeoff between the throughput of samples that an RL agent can learn from (sample throughput) and the quality of learning from each sample (sample efficiency).', 'In these scalable RL architectures, as one increases sample throughput (i.e. increasing parallelization in IMPALA (Espeholt et al., 2018)), sample efficiency drops significantly.', 'To address this, we propose a new distributed reinforcement learning algorithm, IMPACT.', 'IMPACT extends PPO with three changes: a target network for stabilizing the surrogate objective, a circular buffer, and truncated importance sampling.', 'In discrete action-space environments, we show that IMPACT attains higher reward and, simultaneously, achieves up to 30% decrease in training wall-time than that of IMPALA.', 'For continuous control environments, IMPACT trains faster than existing scalable agents while preserving the sample efficiency of synchronous PPO.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['In this paper, we show that a simple coloring scheme can improve, both theoretically and empirically, the expressive power of Message Passing Neural Networks (MPNNs).', 'More specifically, we introduce a graph neural network called Colored Local Iterative Procedure (CLIP) that uses colors to disambiguate identical node attributes, and show that this representation is a universal approximator of continuous functions on graphs with node attributes.', 'Our method relies on separability, a key topological characteristic that allows to extend well-chosen neural networks into universal representations.', 'Finally, we show experimentally that CLIP is capable of capturing structural characteristics that traditional MPNNs fail to distinguish, while being state-of-the-art on benchmark graph classification datasets.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['In this paper we present a method for algorithmic melody generation using a generative adversarial network without recurrent components.', 'Music generation has been successfully done using recurrent neural networks, where the model learns sequence information that can help create authentic sounding melodies. ', 'Here, we use DCGAN architecture with dilated convolutions and towers to capture sequential information as spatial image information, and learn long-range dependencies in fixed-length melody forms such as Irish traditional reel.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Neural machine translation (NMT) systems have reached state of the art performance in translating text and widely deployed. ', 'Yet little is understood about how these systems function or break. ', 'Here we show that NMT systems are susceptible to producing highly pathological translations that are completely untethered from the source material, which we term hallucinations. ', 'Such pathological translations are problematic because they are are deeply disturbing of user trust and easy to find. ', 'We describe a method t generate hallucinations and show that many common variations of the NMT architecture are susceptible to them.', 'We study a variety of approaches to reduce the frequency of hallucinations, including data augmentation, dynamical systems and regularization techniques and show that data augmentation significantly reduces hallucination frequency.', 'Finally, we analyze networks that produce hallucinations and show signatures of hallucinations in the attention matrix and in the stability measures of the decoder.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['For AI systems to garner widespread public acceptance, we must develop methods capable of explaining the decisions of black-box models such as neural networks.', 'In this work, we identify two issues of current explanatory methods.', 'First, we show that two prevalent perspectives on explanations—feature-additivity and feature-selection—lead to fundamentally different instance-wise explanations.', 'In the literature, explainers from different perspectives are currently being directly compared, despite their distinct explanation goals.', 'The second issue is that current post-hoc explainers have only been thoroughly validated on simple models, such as linear regression, and, when applied to real-world neural networks, explainers are commonly evaluated under the assumption that the learned models behave reasonably.', 'However, neural networks often rely on unreasonable correlations, even when producing correct decisions.', 'We introduce a verification framework for explanatory methods under the feature-selection perspective.', 'Our framework is based on a non-trivial neural network architecture trained on a real-world task, and for which we are able to provide guarantees on its inner workings.', 'We validate the efficacy of our evaluation by showing the failure modes of current explainers.', 'We aim for this framework to provide a publicly available,1 off-the-shelf evaluation when the feature-selection perspective on explanations is needed.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Planning in high-dimensional space remains a challenging problem, even with recent advances in algorithms and computational power.', 'We are inspired by efference copy and sensory reafference theory from neuroscience. ', 'Our aim is to allow agents to form mental models of their environments for planning. ', 'The cerebellum is emulated with a two-stream, fully connected, predictor network.', 'The network receives as inputs the efference as well as the features of the current state.', 'Building on insights gained from knowledge distillation methods, we choose as our features the outputs of a pre-trained network, yielding a compressed representation of the current state. ', 'The representation is chosen such that it allows for fast search using classical graph search algorithms.', 'We display the effectiveness of our approach on a viewpoint-matching task using a modified best-first search algorithm.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Experimental evidence indicates that simple models outperform complex deep networks on many unsupervised similarity tasks.', 'Introducing the concept of an optimal representation space, we provide a simple theoretical resolution to this apparent paradox.', 'In addition, we present a straightforward procedure that, without any retraining or architectural modifications, allows deep recurrent models to perform equally well (and sometimes better) when compared to shallow models.', 'To validate our analysis, we conduct a set of consistent empirical evaluations and introduce several new sentence embedding models in the process.', 'Even though this work is presented within the context of natural language processing, the insights are readily applicable to other domains that rely on distributed representations for transfer tasks.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ["Cloze test is widely adopted in language exams to evaluate students' language proficiency.", 'In this paper, we propose the first large-scale human-designed cloze test dataset CLOTH in which the questions were used in middle-school and high-school language exams.', 'With the missing blanks carefully created by teachers and candidate choices purposely designed to be confusing, CLOTH requires a deeper language understanding and a wider attention span than previous automatically generated cloze datasets.', 'We show humans outperform dedicated designed baseline models by a significant margin, even when the model is trained on sufficiently large external data.', 'We investigate the source of the performance gap, trace model deficiencies to some distinct properties of CLOTH, and identify the limited ability of comprehending a long-term context to be the key bottleneck.', "In addition, we find that human-designed data leads to a larger gap between the model's performance and human performance when compared to automatically generated data."] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Recent work suggests goal-driven training of neural networks can be used to model neural activity in the brain.', 'While response properties of neurons in artificial neural networks bear similarities to those in the brain, the network architectures are often constrained to be different.', 'Here we ask if a neural network can recover both neural representations and, if the architecture is unconstrained and optimized, also the anatomical properties of neural circuits.', 'We demonstrate this in a system where the connectivity and the functional organization have been characterized, namely, the head direction circuit of the rodent and fruit fly.', 'We trained recurrent neural networks (RNNs) to estimate head direction through integration of angular velocity.', 'We found that the two distinct classes of neurons observed in the head direction system, the Ring neurons and the Shifter neurons, emerged naturally in artificial neural networks as a result of training.', 'Furthermore, connectivity analysis and in-silico neurophysiology revealed structural and mechanistic similarities between artificial networks and the head direction system.', 'Overall, our results show that optimization of RNNs in a goal-driven task can recapitulate the structure and function of biological circuits, suggesting that artificial neural networks can be used to study the brain at the level of both neural activity and anatomical organization.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Convolutional Neural Networks (CNNs) are computationally intensive, which limits their application on mobile devices.', 'Their energy is dominated by the number of multiplies needed to perform the convolutions.', 'Winograd’s minimal filtering algorithm (Lavin, 2015) and network pruning (Han et al., 2015) can reduce the operation count, but these two methods cannot be straightforwardly combined — applying the Winograd transform fills in the sparsity in both the weights and the activations.', 'We propose two modifications to Winograd-based CNNs to enable these methods to exploit sparsity.', 'First, we move the ReLU operation into the Winograd domain to increase the sparsity of the transformed activations.', 'Second, we prune the weights in the Winograd domain to exploit static weight sparsity.', 'For models on CIFAR-10, CIFAR-100 and ImageNet datasets, our method reduces the number of multiplications by 10.4x, 6.8x and 10.8x respectively with loss of accuracy less than 0.1%, outperforming previous baselines by 2.0x-3.0x.', 'We also show that moving ReLU to the Winograd domain allows more aggressive pruning.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['In this paper we present a novel optimization algorithm called Advanced Neuroevolution.', 'The aim for this algorithm is to train deep neural networks, and eventually act as an alternative to Stochastic Gradient Descent (SGD) and its variants as needed.We evaluated our algorithm on the MNIST dataset, as well as on several global optimization problems such as the Ackley function.', 'We find the algorithm performing relatively well for both cases, overtaking other global optimization algorithms such as Particle Swarm Optimization (PSO) and Evolution Strategies (ES).\n'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Stochastic neural net weights are used in a variety of contexts, including regularization, Bayesian neural nets, exploration in reinforcement learning, and evolution strategies.', 'Unfortunately, due to the large number of weights, all the examples in a mini-batch typically share the same weight perturbation, thereby limiting the variance reduction effect of large mini-batches.', 'We introduce flipout, an efficient method for decorrelating the gradients within a mini-batch by implicitly sampling pseudo-independent weight perturbations for each example.', 'Empirically, flipout achieves the ideal linear variance reduction for fully connected networks, convolutional networks, and RNNs.', 'We find significant speedups in training neural networks with multiplicative Gaussian perturbations.', 'We show that flipout is effective at regularizing LSTMs, and outperforms previous methods.', 'Flipout also enables us to vectorize evolution strategies: in our experiments, a single GPU with flipout can handle the same throughput as at least 40 CPU cores using existing methods, equivalent to a factor-of-4 cost reduction on Amazon Web Services.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Deep generative models such as Variational AutoEncoder (VAE) and Generative Adversarial Network (GAN) play an increasingly important role in machine learning and computer vision.', 'However, there are two fundamental issues hindering their real-world applications: the difficulty of conducting variational inference in VAE and the functional absence of encoding real-world samples in GAN.', 'In this paper, we propose a novel algorithm named Latently Invertible Autoencoder (LIA) to address the above two issues in one framework.', 'An invertible network and its inverse mapping are symmetrically embedded in the latent space of VAE.', 'Thus the partial encoder first transforms the input into feature vectors and then the distribution of these feature vectors is reshaped to fit a prior by the invertible network.', "The decoder proceeds in the reverse order of the encoder's composite mappings.", 'A two-stage stochasticity-free training scheme is designed to train LIA via adversarial learning, in the sense that the decoder of LIA is first trained as a standard GAN with the invertible network and then the partial encoder is learned from an autoencoder by detaching the invertible network from LIA. ', 'Experiments conducted on the FFHQ face dataset and three LSUN datasets validate the effectiveness of LIA for inference and generation.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Neural programs are highly accurate and structured policies that perform algorithmic tasks by controlling the behavior of a computation mechanism.', 'Despite the potential to increase the interpretability and the compositionality of the behavior of artificial agents, it remains difficult to learn from demonstrations neural networks that represent computer programs.', 'The main challenges that set algorithmic domains apart from other imitation learning domains are the need for high accuracy, the involvement of specific structures of data, and the extremely limited observability.', 'To address these challenges, we propose to model programs as Parametrized Hierarchical Procedures (PHPs).', 'A PHP is a sequence of conditional operations, using a program counter along with the observation to select between taking an elementary action, invoking another PHP as a sub-procedure, and returning to the caller.', 'We develop an algorithm for training PHPs from a set of supervisor demonstrations, only some of which are annotated with the internal call structure, and apply it to efficient level-wise training of multi-level PHPs.', 'We show in two benchmarks, NanoCraft and long-hand addition, that PHPs can learn neural programs more accurately from smaller amounts of both annotated and unannotated demonstrations.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Deep Reinforcement Learning has managed to achieve state-of-the-art results in learning control policies directly from raw pixels.', 'However, despite its remarkable success, it fails to generalize, a fundamental component required in a stable Artificial Intelligence system.', 'Using the Atari game Breakout, we demonstrate the difficulty of a trained agent in adjusting to simple modifications in the raw image, ones that a human could adapt to trivially.', 'In transfer learning, the goal is to use the knowledge gained from the source task to make the training of the target task faster and better.', 'We show that using various forms of fine-tuning, a common method for transfer learning, is not effective for adapting to such small visual changes.', 'In fact, it is often easier to re-train the agent from scratch than to fine-tune a trained agent.', 'We suggest that in some cases transfer learning can be improved by adding a dedicated component whose goal is to learn to visually map between the known domain and the new one.', 'Concretely, we use Unaligned Generative Adversarial Networks (GANs) to create a mapping function to translate images in the target task to corresponding images in the source task.', 'These mapping functions allow us to transform between various variations of the Breakout game, as well as between different levels of a Nintendo game, Road Fighter.', 'We show that learning this mapping is substantially more efficient than re-training.', 'A visualization of a trained agent playing Breakout and Road Fighter, with and without the GAN transfer, can be seen in \\url{https://streamable.com/msgtm} and \\url{https://streamable.com/5e2ka}.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['A commonplace belief in the machine learning community is that using adaptive gradient methods hurts generalization.', 'We re-examine this belief both theoretically and experimentally, in light of insights and trends from recent years.\n', 'We revisit some previous oft-cited experiments and theoretical accounts in more depth, and provide a new set of experiments in larger-scale, state-of-the-art settings.', 'We conclude that with proper tuning, the improved training performance of adaptive optimizers does not in general carry an overfitting penalty, especially in contemporary deep learning.', "Finally, we synthesize a ``user's guide'' to adaptive optimizers, including some proposed modifications to AdaGrad to mitigate some of its empirical shortcomings."] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['The ability to generalize quickly from few observations is crucial for intelligent systems.', 'In this paper we introduce APL, an algorithm that approximates probability distributions by remembering the most surprising observations it has encountered.', 'These past observations are recalled from an external memory module and processed by a decoder network that can combine information from different memory slots to generalize beyond direct recall.', 'We show this algorithm can perform as well as state of the art baselines on few-shot classification benchmarks with a smaller memory footprint. ', 'In addition, its memory compression allows it to scale to thousands of unknown labels. ', 'Finally, we introduce a meta-learning reasoning task which is more challenging than direct classification.', 'In this setting, APL is able to generalize with fewer than one example per class via deductive reasoning.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Recent advances in recurrent neural nets (RNNs) have shown much promise in many applications in natural language processing.', 'For most of these tasks, such as sentiment analysis of customer reviews, a recurrent neural net model parses the entire review before forming a decision.', 'We argue that reading the entire input is not always necessary in practice, since a lot of reviews are often easy to classify, i.e., a decision can be formed after reading some crucial sentences or words in the provided text.', 'In this paper, we present an approach of fast reading for text classification.', 'Inspired by several well-known human reading techniques, our approach implements an intelligent recurrent agent which evaluates the importance of the current snippet in order to decide whether to make a prediction, or to skip some texts, or to re-read part of the sentence.', 'Our agent uses an RNN module to encode information from the past and the current tokens, and applies a policy module to form decisions.', 'With an end-to-end training algorithm based on policy gradient, we train and test our agent on several text classification datasets and achieve both higher efficiency and better accuracy compared to previous approaches. \n'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Reinforcement learning agents need to explore their unknown environments to solve the tasks given to them.', 'The Bayes optimal solution to exploration is intractable for complex environments, and while several exploration methods have been proposed as approximations, it remains unclear what underlying objective is being optimized by existing exploration methods, or how they can be altered to incorporate prior knowledge about the task.', 'Moreover, it is unclear how to acquire a single exploration strategy that will be useful for solving multiple downstream tasks.', 'We address these shortcomings by learning a single exploration policy that can quickly solve a suite of downstream tasks in a multi-task setting, amortizing the cost of learning to explore.', 'We recast exploration as a problem of State Marginal Matching (SMM), where we aim to learn a policy for which the state marginal distribution matches a given target state distribution, which can incorporate prior knowledge about the task.', 'We optimize the objective by reducing it to a two-player, zero-sum game between a state density model and a parametric policy.', "Our theoretical analysis of this approach suggests that prior exploration methods do not learn a policy that does distribution matching, but acquire a replay buffer that performs distribution matching, an observation that potentially explains these prior methods' success in single-task settings.", 'On both simulated and real-world tasks, we demonstrate that our algorithm explores faster and adapts more quickly than prior methods.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['The effectiveness of Convolutional Neural Networks stems in large part from their ability to exploit the translation invariance that is inherent in many learning problems.', 'Recently, it was shown that CNNs can exploit other invariances, such as rotation invariance, by using group convolutions instead of planar convolutions.', 'However, for reasons of performance and ease of implementation, it has been necessary to limit the group convolution to transformations that can be applied to the filters without interpolation.', 'Thus, for images with square pixels, only integer translations, rotations by multiples of 90 degrees, and reflections are admissible.\n\n', 'Whereas the square tiling provides a 4-fold rotational symmetry, a hexagonal tiling of the plane has a 6-fold rotational symmetry.', 'In this paper we show how one can efficiently implement planar convolution and group convolution over hexagonal lattices, by re-using existing highly optimized convolution routines.', 'We find that, due to the reduced anisotropy of hexagonal filters, planar HexaConv provides better accuracy than planar convolution with square filters, given a fixed parameter budget.', 'Furthermore, we find that the increased degree of symmetry of the hexagonal grid increases the effectiveness of group convolutions, by allowing for more parameter sharing.', 'We show that our method significantly outperforms conventional CNNs on the AID aerial scene classification dataset, even outperforming ImageNet pre-trained models.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['Deep Convolutional Neural Networks (CNNs) have been repeatedly shown to perform well on image classification tasks, successfully recognizing a broad array of objects when given sufficient training data.', 'Methods for object localization, however, are still in need of substantial improvement.', 'Common approaches to this problem involve the use of a sliding window, sometimes at multiple scales, providing input to a deep CNN trained to classify the contents of the window.', 'In general, these approaches are time consuming, requiring many classification calculations.', 'In this paper, we offer a fundamentally different approach to the localization of recognized objects in images.', 'Our method is predicated on the idea that a deep CNN capable of recognizing an object must implicitly contain knowledge about object location in its connection weights.', 'We provide a simple method to interpret classifier weights in the context of individual classified images.', 'This method involves the calculation of the derivative of network generated activation patterns, such as the activation of output class label units, with regard to each in- put pixel, performing a sensitivity analysis that identifies the pixels that, in a local sense, have the greatest influence on internal representations and object recognition.', 'These derivatives can be efficiently computed using a single backward pass through the deep CNN classifier, producing a sensitivity map of the image.', 'We demonstrate that a simple linear mapping can be learned from sensitivity maps to bounding box coordinates, localizing the recognized object.', 'Our experimental results, using real-world data sets for which ground truth localization information is known, reveal competitive accuracy from our fast technique.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['We present trellis networks, a new architecture for sequence modeling.', 'On the one hand, a trellis network is a temporal convolutional network with special structure, characterized by weight tying across depth and direct injection of the input into deep layers.', 'On the other hand, we show that truncated recurrent networks are equivalent to trellis networks with special sparsity structure in their weight matrices.', 'Thus trellis networks with general weight matrices generalize truncated recurrent networks.', 'We leverage these connections to design high-performing trellis networks that absorb structural and algorithmic elements from both recurrent and convolutional models.', 'Experiments demonstrate that trellis networks outperform the current state of the art methods on a variety of challenging benchmarks, including word-level language modeling and character-level language modeling tasks, and stress tests designed to evaluate long-term memory retention.', 'The code is available at https://github.com/locuslab/trellisnet .'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['We propose an end-to-end framework for training domain specific models (DSMs) to obtain both high accuracy and computational efficiency for object detection tasks.', 'DSMs are trained with distillation and focus on achieving high accuracy at a limited domain (e.g. fixed view of an intersection).', 'We argue that DSMs can capture essential features well even with a small model size, enabling higher accuracy and efficiency than traditional techniques. ', 'In addition, we improve the training efficiency by reducing the dataset size by culling easy to classify images from the training set.', 'For the limited domain, we observed that compact DSMs significantly surpass the accuracy of COCO trained models of the same size.', 'By training on a compact dataset, we show that with an accuracy drop of only 3.6%, the training time can be reduced by 93%.'] ### SUMMARY:

### Instruction->PROVIDE ME WITH SUMMARY FOR THE GIVEN INPUT WHILE KEEPING THE MOST IMPORTANT DETAILS INTACT: ['We compare the model-free reinforcement learning with the model-based approaches through the lens of the expressive power of neural networks for policies, $Q$-functions, and dynamics. ', 'We show, theoretically and empirically, that even for one-dimensional continuous state space, there are many MDPs whose optimal $Q$-functions and policies are much more complex than the dynamics.', 'We hypothesize many real-world MDPs also have a similar property.', 'For these MDPs, model-based planning is a favorable algorithm, because the resulting policies can approximate the optimal policy significantly better than a neural network parameterization can, and model-free or model-based policy optimization rely on policy parameterization.', 'Motivated by the theory, we apply a simple multi-step model-based bootstrapping planner (BOOTS) to bootstrap a weak $Q$-function into a stronger policy.', 'Empirical results show that applying BOOTS on top of model-based or model-free policy optimization algorithms at the test time improves the performance on MuJoCo benchmark tasks.'] ### SUMMARY: