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machine translationese

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machine translation

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translation machine translation

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0.2850789427757263]

purpose data augmentation

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-0.6345106363296509, -0.3883303999900818, 0.3652819097042084]

data augmentation model

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data augmentation learning

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data augmentation benefit

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data augmentation

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semantic role labeling

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-0.5681672692298889, -1.1809451580047607, -0.5233638882637024, 0.5657004117965698]

automatic object detection

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0.8557407259941101]

object detection fsvod

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0.5252754092216492]

object detection sod

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1.0999767780303955]

object detection cova

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0.08508890867233276, 0.9208670258522034]

object detection tod

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one stage object detection

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bottom up object detection

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object detection

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human object interaction detection

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two stage object detection

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restricted boltzmann machine based

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restricted boltzmann machine

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-0.8816631436347961, -0.42273974418640137]

generalized boltzmann machine

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full boltzmann machine

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boltzmann machine dbm

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-0.11141335964202881, 0.19292068481445312, -0.3790365755558014, -0.48002681136131287, -0.015931442379951477]

multiple benchmark datasets

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large neural network

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large neural net

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computer vision problem

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gradient descent method

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learning method

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extended differential privacy

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differential privacy feature

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differential privacy dynamic

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intrinsic differential privacy

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differential privacy based

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partial differential privacy

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smoothed differential privacy

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differential privacy

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improved differential privacy

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constrained differential privacy

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differential privacy noise

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domain knowledge

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1.1060678958892822]

strongly related problem

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challenging problem

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natural language query

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-0.12586212158203125]

computer vision technique

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neural network structure

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0.46438875794410706]

data augmentation strategy

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face recognition system

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time series classification

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time series classification algorithm

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time series classification approach

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time series classification problem

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time series recognition

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dpn cnn

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pointcloud based place recognition

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pairwise point cloud registration

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-0.5369168519973755, 0.19286426901817322]

hybrid deep model

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deep model

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statistical machine translation

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neural model

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sentimental analysis

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sentiment analysis corpus

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sentiment analysis

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-1.3445954322814941, 0.5695921182632446]

entitled sentiment analysis

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0.47725099325180054]

q function

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-0.3955425024032593, -0.5005069971084595]

reward function

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reward gradient

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q functions

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-1.0555272102355957, -0.4311784505844116, -0.494778573513031]

reproducible human evaluation

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0.478964239358902]

human evaluation

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random forest

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-1.0578546524047852, -0.9255523085594177, 0.03288887068629265]

random forest learning

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-0.977624773979187, -0.8808786273002625, 0.12906411290168762]

benchmark dataset

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linear function approximation

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loss function

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computational complexity aspect

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complexity and computational

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computational complexity

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involved computational complexity

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-0.6779265999794006, -0.9815062284469604, 0.16970571875572205]

object detection task

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hierarchical reinforcement learning

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-0.037757791578769684, -0.10783933103084564]

quantum machine learning

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