https://github.com/MachineLearningSystem/awesome-mixture-of-experts

A collection of AWESOME things about mixture-of-experts
https://github.com/MachineLearningSystem/awesome-mixture-of-experts

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A collection of AWESOME things about mixture-of-experts

• Host: GitHub
• URL: https://github.com/MachineLearningSystem/awesome-mixture-of-experts
• Owner: MachineLearningSystem
• Fork: true (XueFuzhao/awesome-mixture-of-experts)
• Created: 2023-02-17T03:24:49.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2022-11-30T08:16:41.000Z (almost 2 years ago)
• Last Synced: 2024-05-23T02:09:49.531Z (6 months ago)
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  • Readme: README.md

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• awesome-AI-system - awesome MoE

README

        
# awesome-mixture-of-experts [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)

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A collection of AWESOME things about mixture-of-experts

This repo is a collection of AWESOME things about mixture-of-experts, including papers, code, etc. Feel free to star and fork.

# Contents

- [awesome-mixture-of-experts](#awesome-mixture-of-experts)

- [Contents](#contents)

- [Papers](#papers)

  - [MoE Model](#moe-model)

  - [MoE System](#moe-system)

- [Library](#library)

# Papers

## Great Survey

- A Review of Sparse Expert Models in Deep Learning [[4 Sep 2022]](https://arxiv.org/abs/2209.01667)

## MoE Model

**Publication**

- Taming Sparsely Activated Transformer with Stochastic Experts [[ICLR 2022]](https://arxiv.org/abs/2110.04260)

- Go Wider Instead of Deeper [[AAAI2022]](https://arxiv.org/abs/2107.11817)

- Hash layers for large sparse models [[NeurIPS2021]](https://arxiv.org/abs/2106.04426)

- DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning [[NeurIPS2021]](https://arxiv.org/abs/2106.03760)

- Scaling Vision with Sparse Mixture of Experts [[NeurIPS2021]](https://proceedings.neurips.cc/paper/2021/hash/48237d9f2dea8c74c2a72126cf63d933-Abstract.html)

- BASE Layers: Simplifying Training of Large, Sparse Models [[ICML2021]](https://arxiv.org/abs/2103.16716)

- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer [[ICLR2017]](https://openreview.net/forum?id=B1ckMDqlg)

- CPM-2: Large-scale cost-effective pre-trained language models [[AI Open]](https://www.sciencedirect.com/science/article/pii/S2666651021000310)

- Mixture of experts: a literature survey [[Artificial Intelligence Review]](https://link.springer.com/article/10.1007/s10462-012-9338-y)

**arXiv**

- MoEC: Mixture of Expert Clusters [[19 Jul 2022]](https://arxiv.org/abs/2207.09094)

- No Language Left Behind: Scaling Human-Centered Machine Translation [[6 Jul 2022]](https://research.facebook.com/publications/no-language-left-behind/)

- Sparse Fusion Mixture-of-Experts are Domain Generalizable Learners [[8 Jun 2022]](https://arxiv.org/abs/2206.04046)

- Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts [[6 Jun 2022]](https://arxiv.org/abs/2206.02770)

- Patcher: Patch Transformers with Mixture of Experts for Precise Medical Image Segmentation [[5 Jun 2022]](https://arxiv.org/abs/2206.01741)

- Interpretable Mixture of Experts for Structured Data [[5 Jun 2022]](https://arxiv.org/abs/2206.02107)

- Task-Specific Expert Pruning for Sparse Mixture-of-Experts [[1 Jun 2022]](https://arxiv.org/abs/2206.00277)

- Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers [[28 May 2022]](https://arxiv.org/abs/2205.14336)

- AdaMix: Mixture-of-Adapter for Parameter-efficient Tuning of Large Language Models [[24 May 2022]](https://arxiv.org/abs/2205.12399)

- Sparse Mixers: Combining MoE and Mixing to build a more efficient BERT [[24 May 2022]](https://arxiv.org/abs/2205.12399)

- One Model, Multiple Modalities: A Sparsely Activated Approach for Text, Sound, Image, Video and Code [[12 May 2022]](https://arxiv.org/abs/2205.06126)

- SkillNet-NLG: General-Purpose Natural Language Generation with a Sparsely Activated Approach [[26 Apr 2022]](https://arxiv.org/abs/2204.12184)

- Residual Mixture of Experts [[20 Apr 2022]](https://arxiv.org/abs/2204.09636)

- On the Representation Collapse of Sparse Mixture of Experts [[20 Apr 2022]](https://arxiv.org/abs/2204.09179)

- StableMoE: Stable Routing Strategy for Mixture of Experts [[18 Apr 2022]](https://arxiv.org/abs/2204.08396)

- Sparsely Activated Mixture-of-Experts are Robust Multi-Task Learners [[16 Apr 2022]](https://arxiv.org/abs/2204.07689)

- MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation [[15 Apr 2022]](https://arxiv.org/abs/2204.07675)

- Mixture-of-experts VAEs can disregard variation in surjective multimodal data [[11 Apr 2022]](https://arxiv.org/abs/2204.05229)

- Efficient Language Modeling with Sparse all-MLP [[14 Mar 2022]](https://arxiv.org/abs/2203.06850)

- Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models [[2 Mar 2022]](https://arxiv.org/abs/2203.01104)

- Mixture-of-Experts with Expert Choice Routing [[18 Feb 2022]](https://arxiv.org/abs/2101.03961)

- Designing Effective Sparse Expert Models [[17 Feb 2022]](https://arxiv.org/abs/2202.08906)

- Unified Scaling Laws for Routed Language Models [[2 Feb 2022]](https://arxiv.org/abs/2202.01169)

- Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model [[28 Jan 2022]](https://arxiv.org/abs/2201.11990)

- One Student Knows All Experts Know: From Sparse to Dense [[26 Jan 2022]](https://arxiv.org/abs/2201.10890)

- Dense-to-Sparse Gate for Mixture-of-Experts [[29 Dec 2021]](https://arxiv.org/abs/2112.14397)

- Efficient Large Scale Language Modeling with Mixtures of Experts [[20 Dec 2021]](https://arxiv.org/abs/2112.10684)

- GLaM: Efficient Scaling of Language Models with Mixture-of-Experts [[13 Dec 2021]](https://arxiv.org/abs/2112.06905)

- Building a great multi-lingual teacher with sparsely-gated mixture of experts for speech recognition [[10 Dec 2021]](https://arxiv.org/abs/2112.05820)

- SpeechMoE2: Mixture-of-Experts Model with Improved Routing [[23 Nov 2021]](https://arxiv.org/abs/2111.11831)

- VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts [[23 Nov 2021]](https://arxiv.org/abs/2111.02358)

- Towards More Effective and Economic Sparsely-Activated Model [[14 Oct 2021]](https://arxiv.org/abs/2110.07431)

- M6-10T: A Sharing-Delinking Paradigm for Efficient Multi-Trillion Parameter Pretraining [[8 Oct 2021]](https://arxiv.org/abs/2110.03888)

- Sparse MoEs meet Efficient Ensembles [[7 Oct 2021]](https://arxiv.org/abs/2110.03360)

- MoEfication: Conditional Computation of Transformer Models for Efficient Inference [[5 Oct 2021]](https://arxiv.org/abs/2110.01786)

- Cross-token Modeling with Conditional Computation [[5 Sep 2021]](https://arxiv.org/abs/2109.02008)

- M6-T: Exploring Sparse Expert Models and Beyond [[31 May 2021]](https://arxiv.org/abs/2105.15082)

- SpeechMoE: Scaling to Large Acoustic Models with Dynamic Routing Mixture of Experts [[7 May 2021]](https://arxiv.org/abs/2105.03036)

- Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity [[11 Jan 2021]](https://arxiv.org/abs/2101.03961)

- Exploring Routing Strategies for Multilingual Mixture-of-Experts Models [[28 Sept 2020]](https://openreview.net/forum?id=ey1XXNzcIZS)

## MoE System

**Publication**

- Pathways: Asynchronous Distributed Dataflow for ML [[MLSys2022]](https://arxiv.org/abs/2203.12533)

- Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning [[OSDI2022]](https://arxiv.org/abs/2201.12023)

- FasterMoE: modeling and optimizing training of large-scale dynamic pre-trained models[[PPoPP2022]](https://dl.acm.org/doi/abs/10.1145/3503221.3508418)

- BaGuaLu: Targeting Brain Scale Pretrained Models with over 37 Million Cores [[PPoPP2022]](http://keg.cs.tsinghua.edu.cn/jietang/publications/PPOPP22-Ma%20et%20al.-BaGuaLu%20Targeting%20Brain%20Scale%20Pretrained%20Models%20w.pdf)

- GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding [[ICLR2021]](https://openreview.net/forum?id=qrwe7XHTmYb)

**arXiv**

- MegaBlocks: Efficient Sparse Training with Mixture-of-Experts [[29 Nov 2022]](https://arxiv.org/abs/2211.15841)

- HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System [[28 Mar 2022]](https://arxiv.org/abs/2203.14685)

- SE-MoE: A Scalable and Efficient Mixture-of-Experts Distributed Training and Inference System [[20 Mar 2022]](https://arxiv.org/abs/2205.10034)

- DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale [[14 Jan 2022]](https://arxiv.org/abs/2201.05596)

- SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient [[29 Sep 2021]](https://openreview.net/forum?id=U1edbV4kNu_)

- FastMoE: A Fast Mixture-of-Expert Training System [[24 Mar 2021]](https://arxiv.org/abs/2103.13262)

## MoE Application

**arXiv**

- Spatial Mixture-of-Experts [[24 Nov 2022]](https://arxiv.org/abs/2211.13491)

- A Mixture-of-Expert Approach to RL-based Dialogue Management [[31 May 2022]](https://arxiv.org/abs/2206.00059)

- Pluralistic Image Completion with Probabilistic Mixture-of-Experts [[18 May 2022]](https://arxiv.org/abs/2205.09086)

- ST-ExpertNet: A Deep Expert Framework for Traffic Prediction [[5 May 2022]](https://arxiv.org/abs/2205.07851)

- Build a Robust QA System with Transformer-based Mixture of Experts [[20 Mar 2022]](https://arxiv.org/abs/2204.09598)

- Mixture of Experts for Biomedical Question Answering [[15 Apr 2022]](https://arxiv.org/abs/2204.07469)

# Library

- [Tutel: An efficient mixture-of-experts implementation for large DNN model training](https://github.com/microsoft/tutel)

- [Mesh-TensorFlow: Deep Learning for Supercomputers](https://github.com/tensorflow/mesh)

- [FastMoE: A Fast Mixture-of-Expert Training System](https://github.com/laekov/fastmoe)

- [DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale](https://github.com/microsoft/DeepSpeed)