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Collections including paper arxiv:2310.10837

mixture-of-experts

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

Paper • 1701.06538 • Published Jan 23, 2017 • 6
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 • 5
A Mixture of h-1 Heads is Better than h Heads

Paper • 2005.06537 • Published May 13, 2020 • 2

MoEs papers reading list

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

Paper • 1701.06538 • Published Jan 23, 2017 • 6
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 • 5
A Mixture of h-1 Heads is Better than h Heads

Paper • 2005.06537 • Published May 13, 2020 • 2

Scaling MLPs: A Tale of Inductive Bias

Paper • 2306.13575 • Published Jun 23, 2023 • 14
Trap of Feature Diversity in the Learning of MLPs

Paper • 2112.00980 • Published Dec 2, 2021 • 1
Understanding the Spectral Bias of Coordinate Based MLPs Via Training Dynamics

Paper • 2301.05816 • Published Jan 14, 2023 • 1
RaftMLP: How Much Can Be Done Without Attention and with Less Spatial Locality?

Paper • 2108.04384 • Published Aug 9, 2021 • 1

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

Paper • 2310.16795 • Published Oct 25, 2023 • 27
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

Approximating Two-Layer Feedforward Networks for Efficient Transformers

Paper • 2310.10837 • Published Oct 16, 2023 • 11
BitNet: Scaling 1-bit Transformers for Large Language Models

Paper • 2310.11453 • Published Oct 17, 2023 • 97
QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models

Paper • 2310.16795 • Published Oct 25, 2023 • 27
LLM-FP4: 4-Bit Floating-Point Quantized Transformers

Paper • 2310.16836 • Published Oct 25, 2023 • 14

vision-transformer

Vision Transformer Adapters for Generalizable Multitask Learning

Paper • 2308.12372 • Published Aug 23, 2023
RMT: Retentive Networks Meet Vision Transformers

Paper • 2309.11523 • Published Sep 20, 2023 • 33
DualToken-ViT: Position-aware Efficient Vision Transformer with Dual Token Fusion

Paper • 2309.12424 • Published Sep 21, 2023 • 11
PaLI-3 Vision Language Models: Smaller, Faster, Stronger

Paper • 2310.09199 • Published Oct 13, 2023 • 27

Uncovering mesa-optimization algorithms in Transformers

Paper • 2309.05858 • Published Sep 11, 2023 • 13
ProPainter: Improving Propagation and Transformer for Video Inpainting

Paper • 2309.03897 • Published Sep 7, 2023 • 27
Approximating Two-Layer Feedforward Networks for Efficient Transformers

Paper • 2310.10837 • Published Oct 16, 2023 • 11
CLEX: Continuous Length Extrapolation for Large Language Models

Paper • 2310.16450 • Published Oct 25, 2023 • 10

MADLAD-400: A Multilingual And Document-Level Large Audited Dataset

Paper • 2309.04662 • Published Sep 9, 2023 • 23
Neurons in Large Language Models: Dead, N-gram, Positional

Paper • 2309.04827 • Published Sep 9, 2023 • 17
Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs

Paper • 2309.05516 • Published Sep 11, 2023 • 10
DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule Graphs

Paper • 2309.03907 • Published May 18, 2023 • 12

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