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.
- DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models [Jan 2024] Repo Paper
- LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training [Dec 2023] Repo
- Mixtral of Experts [Dec 2023] Repo Paper
- OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models [Aug 2023] Repo Paper
- Efficient Large Scale Language Modeling with Mixtures of Experts [Dec 2021] Repo Paper
- Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity [Feb 2021] Repo Paper
I list my favorite MoE papers here. I think these papers can greatly help new MoErs to know about this topic.
- A Review of Sparse Expert Models in Deep Learning [4 Sep 2022]
- Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity [11 Jan 2021]
- GLaM: Efficient Scaling of Language Models with Mixture-of-Experts [13 Dec 2021]
- Scaling Vision with Sparse Mixture of Experts [NeurIPS2021]
- ST-MoE: Designing Stable and Transferable Sparse Expert Models [17 Feb 2022]
- Mixture-of-Experts with Expert Choice Routing [NeurIPS 2022]
- Brainformers: Trading Simplicity for Efficiency [ICML 2023]
- From Sparse to Soft Mixtures of Experts [2 Aug 2023]
- OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models Aug 2023
Publication
- Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks [ICML 2023]
- Robust Mixture-of-Expert Training for Convolutional Neural Networks [ICCV 2023]
- Merging Experts into One: Improving Computational Efficiency of Mixture of Experts [EMNLP 2023]
- PAD-Net: An Efficient Framework for Dynamic Networks [ACL 2023]
- Brainformers: Trading Simplicity for Efficiency [ICML 2023]
- On the Representation Collapse of Sparse Mixture of Experts [NeurIPS 2022]
- StableMoE: Stable Routing Strategy for Mixture of Experts [ACL 2022]
- Taming Sparsely Activated Transformer with Stochastic Experts [ICLR 2022]
- Go Wider Instead of Deeper [AAAI2022]
- Hash layers for large sparse models [NeurIPS2021]
- DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning [NeurIPS2021]
- Scaling Vision with Sparse Mixture of Experts [NeurIPS2021]
- BASE Layers: Simplifying Training of Large, Sparse Models [ICML2021]
- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer [ICLR2017]
- CPM-2: Large-scale cost-effective pre-trained language models [AI Open]
- Mixture of experts: a literature survey [Artificial Intelligence Review]
arXiv
- MoEC: Mixture of Expert Clusters [19 Jul 2022]
- No Language Left Behind: Scaling Human-Centered Machine Translation [6 Jul 2022]
- Sparse Fusion Mixture-of-Experts are Domain Generalizable Learners [8 Jun 2022]
- Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts [6 Jun 2022]
- Patcher: Patch Transformers with Mixture of Experts for Precise Medical Image Segmentation [5 Jun 2022]
- Interpretable Mixture of Experts for Structured Data [5 Jun 2022]
- Task-Specific Expert Pruning for Sparse Mixture-of-Experts [1 Jun 2022]
- Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers [28 May 2022]
- AdaMix: Mixture-of-Adapter for Parameter-efficient Tuning of Large Language Models [24 May 2022]
- Sparse Mixers: Combining MoE and Mixing to build a more efficient BERT [24 May 2022]
- One Model, Multiple Modalities: A Sparsely Activated Approach for Text, Sound, Image, Video and Code [12 May 2022]
- SkillNet-NLG: General-Purpose Natural Language Generation with a Sparsely Activated Approach [26 Apr 2022]
- Residual Mixture of Experts [20 Apr 2022]
- Sparsely Activated Mixture-of-Experts are Robust Multi-Task Learners [16 Apr 2022]
- MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation [15 Apr 2022]
- Mixture-of-experts VAEs can disregard variation in surjective multimodal data [11 Apr 2022]
- Efficient Language Modeling with Sparse all-MLP [14 Mar 2022]
- Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models [2 Mar 2022]
- Mixture-of-Experts with Expert Choice Routing [18 Feb 2022]
- ST-MoE: Designing Stable and Transferable Sparse Expert Models [17 Feb 2022]
- Designing Effective Sparse Expert Models [17 Feb 2022]
- Unified Scaling Laws for Routed Language Models [2 Feb 2022]
- Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model [28 Jan 2022]
- One Student Knows All Experts Know: From Sparse to Dense [26 Jan 2022]
- Dense-to-Sparse Gate for Mixture-of-Experts [29 Dec 2021]
- Efficient Large Scale Language Modeling with Mixtures of Experts [20 Dec 2021]
- GLaM: Efficient Scaling of Language Models with Mixture-of-Experts [13 Dec 2021]
- Building a great multi-lingual teacher with sparsely-gated mixture of experts for speech recognition [10 Dec 2021]
- SpeechMoE2: Mixture-of-Experts Model with Improved Routing [23 Nov 2021]
- VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts [23 Nov 2021]
- Towards More Effective and Economic Sparsely-Activated Model [14 Oct 2021]
- M6-10T: A Sharing-Delinking Paradigm for Efficient Multi-Trillion Parameter Pretraining [8 Oct 2021]
- Sparse MoEs meet Efficient Ensembles [7 Oct 2021]
- MoEfication: Conditional Computation of Transformer Models for Efficient Inference [5 Oct 2021]
- Cross-token Modeling with Conditional Computation [5 Sep 2021]
- M6-T: Exploring Sparse Expert Models and Beyond [31 May 2021]
- SpeechMoE: Scaling to Large Acoustic Models with Dynamic Routing Mixture of Experts [7 May 2021]
- Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity [11 Jan 2021]
- Exploring Routing Strategies for Multilingual Mixture-of-Experts Models [28 Sept 2020]
Publication
- Pathways: Asynchronous Distributed Dataflow for ML [MLSys2022]
- Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning [OSDI2022]
- FasterMoE: modeling and optimizing training of large-scale dynamic pre-trained models[PPoPP2022]
- BaGuaLu: Targeting Brain Scale Pretrained Models with over 37 Million Cores [PPoPP2022]
- GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding [ICLR2021]
arXiv
- MegaBlocks: Efficient Sparse Training with Mixture-of-Experts [29 Nov 2022]
- HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System [28 Mar 2022]
- SE-MoE: A Scalable and Efficient Mixture-of-Experts Distributed Training and Inference System [20 Mar 2022]
- DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale [14 Jan 2022]
- SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient [29 Sep 2021]
- FastMoE: A Fast Mixture-of-Expert Training System [24 Mar 2021]
Publication
- Switch-NeRF: Learning Scene Decomposition with Mixture of Experts for Large-scale Neural Radiance Fields [02 Feb 2023]
arXiv
- Spatial Mixture-of-Experts [24 Nov 2022]
- A Mixture-of-Expert Approach to RL-based Dialogue Management [31 May 2022]
- Pluralistic Image Completion with Probabilistic Mixture-of-Experts [18 May 2022]
- ST-ExpertNet: A Deep Expert Framework for Traffic Prediction [5 May 2022]
- Build a Robust QA System with Transformer-based Mixture of Experts [20 Mar 2022]
- Mixture of Experts for Biomedical Question Answering [15 Apr 2022]
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