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Collections including paper arxiv:2209.01667

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

Papers - MoE - Research

Adaptive sequential Monte Carlo by means of mixture of experts

Paper • 1108.2836 • Published Aug 14, 2011 • 2
Convergence Rates for Mixture-of-Experts

Paper • 1110.2058 • Published Oct 10, 2011 • 2
Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs

Paper • 2310.12008 • Published Oct 18, 2023 • 2
Enhancing NeRF akin to Enhancing LLMs: Generalizable NeRF Transformer with Mixture-of-View-Experts

Paper • 2308.11793 • Published Aug 22, 2023 • 2

Large Language Model Alignment: A Survey

Paper • 2309.15025 • Published Sep 26, 2023 • 2
Aligning Large Language Models with Human: A Survey

Paper • 2307.12966 • Published Jul 24, 2023 • 1
Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Paper • 2305.18290 • Published May 29, 2023 • 47
SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF

Paper • 2310.05344 • Published Oct 9, 2023 • 1

Mixture of Experts Papers

MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

Paper • 2401.15947 • Published Jan 29 • 48
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Paper • 2401.06066 • Published Jan 11 • 42
SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention

Paper • 2312.07987 • Published Dec 13, 2023 • 40
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

Paper • 2101.03961 • Published Jan 11, 2021 • 14

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 • 4
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 • 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 • 4
A Mixture of h-1 Heads is Better than h Heads

Paper • 2005.06537 • Published May 13, 2020 • 2

Towards an Understanding of Large Language Models in Software Engineering Tasks

Paper • 2308.11396 • Published Aug 22, 2023 • 1
Several categories of Large Language Models (LLMs): A Short Survey

Paper • 2307.10188 • Published Jul 5, 2023 • 1
Large Language Models for Generative Recommendation: A Survey and Visionary Discussions

Paper • 2309.01157 • Published Sep 3, 2023 • 1
A Survey on Large Language Models for Recommendation

Paper • 2305.19860 • Published May 31, 2023 • 1

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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