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Collections including paper arxiv:2106.04426

MoEs papers reading list

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Paper • 1701.06538 • Published Jan 23, 2017 • 4
Sparse Networks from Scratch: Faster Training without Losing Performance

Paper • 1907.04840 • Published Jul 10, 2019 • 3
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

Paper • 1910.02054 • Published Oct 4, 2019 • 3
A Mixture of h-1 Heads is Better than h Heads

Paper • 2005.06537 • Published May 13, 2020 • 2

Papers - MoE - Hashing instead of a Router

Hash Layers For Large Sparse Models

Paper • 2106.04426 • Published Jun 8, 2021 • 2

Papers - Custom Layers - Hash Layers

Hash Layers For Large Sparse Models

Paper • 2106.04426 • Published Jun 8, 2021 • 2

Papers - Training

SELF: Language-Driven Self-Evolution for Large Language Model

Paper • 2310.00533 • Published Oct 1, 2023 • 2
GrowLength: Accelerating LLMs Pretraining by Progressively Growing Training Length

Paper • 2310.00576 • Published Oct 1, 2023 • 2
A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity

Paper • 2305.13169 • Published May 22, 2023 • 3
Transformers Can Achieve Length Generalization But Not Robustly

Paper • 2402.09371 • Published Feb 14 • 12

Papers - Custom Layers

Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning

Paper • 2310.20587 • Published Oct 31, 2023 • 15
JoMA: Demystifying Multilayer Transformers via JOint Dynamics of MLP and Attention

Paper • 2310.00535 • Published Oct 1, 2023 • 2
Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla

Paper • 2307.09458 • Published Jul 18, 2023 • 9
The Impact of Depth and Width on Transformer Language Model Generalization

Paper • 2310.19956 • Published Oct 30, 2023 • 9

Papers - MoE - Router

Turn Waste into Worth: Rectifying Top-k Router of MoE

Paper • 2402.12399 • Published Feb 17 • 2
CompeteSMoE -- Effective Training of Sparse Mixture of Experts via Competition

Paper • 2402.02526 • Published Feb 4 • 3
Buffer Overflow in Mixture of Experts

Paper • 2402.05526 • Published Feb 8 • 8
OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models

Paper • 2402.01739 • Published Jan 29 • 26

Non-asymptotic oracle inequalities for the Lasso in high-dimensional mixture of experts

Paper • 2009.10622 • Published Sep 22, 2020 • 1
MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

Paper • 2401.15947 • Published Jan 29 • 47
MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts

Paper • 2401.04081 • Published Jan 8 • 68
MoE-Infinity: Activation-Aware Expert Offloading for Efficient MoE Serving

Paper • 2401.14361 • Published Jan 25 • 2

Papers - Training Research

Measuring the Effects of Data Parallelism on Neural Network Training

Paper • 1811.03600 • Published Nov 8, 2018 • 2
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost

Paper • 1804.04235 • Published Apr 11, 2018 • 2
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Paper • 1905.11946 • Published May 28, 2019 • 3
Yi: Open Foundation Models by 01.AI

Paper • 2403.04652 • Published Mar 7 • 59

mixture-of-experts

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Paper • 1701.06538 • Published Jan 23, 2017 • 4
Sparse Networks from Scratch: Faster Training without Losing Performance

Paper • 1907.04840 • Published Jul 10, 2019 • 3
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

Paper • 1910.02054 • Published Oct 4, 2019 • 3
A Mixture of h-1 Heads is Better than h Heads

Paper • 2005.06537 • Published May 13, 2020 • 2

MOE papers to read

Copied from MoE using https://huggingface.co/spaces/librarian-bots/collection_cloner.

QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models

Paper • 2310.16795 • Published Oct 25, 2023 • 26
Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference

Paper • 2308.12066 • Published Aug 23, 2023 • 4
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference

Paper • 2303.06182 • Published Mar 10, 2023 • 1
EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate

Paper • 2112.14397 • Published Dec 29, 2021 • 1

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