id string | sources list | title string | abstract string | authors list | categories list | fields_of_study list | published_date timestamp[s] | url string | pdf_url string | arxiv_id string | doi string | citation_count int64 | influential_citation_count int64 | has_code bool | code_url string | venue string | quality_score float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
edbcea7aad07e43aea079497303aae1e574fdcb60eff57d226b6c53c3cb7c002 | [
"arxiv",
"semantic_scholar"
] | A Survey on Mixture of Experts in Large Language Models | Large language models (LLMs) have garnered unprecedented advancements across diverse fields, ranging from natural language processing to computer vision and beyond. The prowess of LLMs is underpinned by their substantial model size, extensive and diverse datasets, and the vast computational power harnessed during train... | [
"Weilin Cai",
"Juyong Jiang",
"Fan Wang",
"Jing Tang",
"Sunghun Kim",
"Jiayi Huang"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2024-06-26T00:00:00 | https://arxiv.org/abs/2407.06204 | https://arxiv.org/pdf/2407.06204v3 | 2407.06204 | 10.1109/TKDE.2025.3554028 | 326 | 19 | true | https://github.com/withinmiaov/A-Survey-on-Mixture-of-Experts-in-LLMs | IEEE Transactions on Knowledge and Data Engineering | 0.6505 |
b9895974b1451933f4d17cc2e145e14ab89622df61d035eb6cdfb784aa31d12a | [
"arxiv",
"semantic_scholar"
] | LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training | Mixture-of-Experts (MoE) has gained increasing popularity as a promising framework for scaling up large language models (LLMs). However, training MoE from scratch in a large-scale setting still suffers from data-hungry and instability problems. Motivated by this limit, we investigate building MoE models from existing d... | [
"Tong Zhu",
"Xiaoye Qu",
"Daize Dong",
"Jiacheng Ruan",
"Jingqi Tong",
"Conghui He",
"Yu Cheng"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-06-24T00:00:00 | https://arxiv.org/abs/2406.16554 | https://arxiv.org/pdf/2406.16554v1 | 2406.16554 | 10.48550/arXiv.2406.16554 | 157 | 9 | true | https://github.com/pjlab-sys4nlp/llama-moe | Conference on Empirical Methods in Natural Language Processing | 0.5497 |
f29ec0e5eefa07eddb35d026cea6bfbee0b3f1e040819b8b2277cb73f116de18 | [
"arxiv",
"semantic_scholar"
] | AdaMoE: Token-Adaptive Routing with Null Experts for Mixture-of-Experts Language Models | Mixture of experts (MoE) has become the standard for constructing production-level large language models (LLMs) due to its promise to boost model capacity without causing significant overheads. Nevertheless, existing MoE methods usually enforce a constant top-k routing for all tokens, which is arguably restrictive beca... | [
"Zihao Zeng",
"Yibo Miao",
"Hongcheng Gao",
"Hao Zhang",
"Zhijie Deng"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2024-06-19T00:00:00 | https://arxiv.org/abs/2406.13233 | https://arxiv.org/pdf/2406.13233v2 | 2406.13233 | 10.48550/arXiv.2406.13233 | 41 | 6 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.4225 |
eff8c2fc1e5bb369d6b3730b1f9f757c2522ca9ecc740253bf493be47add59a7 | [
"arxiv",
"semantic_scholar"
] | $\texttt{MoE-RBench}$: Towards Building Reliable Language Models with Sparse Mixture-of-Experts | Mixture-of-Experts (MoE) has gained increasing popularity as a promising framework for scaling up large language models (LLMs). However, the reliability assessment of MoE lags behind its surging applications. Moreover, when transferred to new domains such as in fine-tuning MoE models sometimes underperform their dense ... | [
"Guanjie Chen",
"Xinyu Zhao",
"Tianlong Chen",
"Yu Cheng"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2024-06-17T00:00:00 | https://arxiv.org/abs/2406.11353 | https://arxiv.org/pdf/2406.11353v1 | 2406.11353 | 10.48550/arXiv.2406.11353 | 9 | 1 | true | https://github.com/UNITES-Lab/MoE-RBench | International Conference on Machine Learning | 0.25 |
91ac4ea42fe5beeee310922eb00669f342befdf84b8d5a5845d0773406a28bfe | [
"arxiv",
"semantic_scholar"
] | Self-MoE: Towards Compositional Large Language Models with Self-Specialized Experts | We present Self-MoE, an approach that transforms a monolithic LLM into a compositional, modular system of self-specialized experts, named MiXSE (MiXture of Self-specialized Experts). Our approach leverages self-specialization, which constructs expert modules using self-generated synthetic data, each equipping a shared ... | [
"Junmo Kang",
"Leonid Karlinsky",
"Hongyin Luo",
"Zhen Wang",
"Jacob Hansen",
"James Glass",
"David Cox",
"Rameswar Panda",
"Rogerio Feris",
"Alan Ritter"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2024-06-17T00:00:00 | https://arxiv.org/abs/2406.12034 | https://arxiv.org/pdf/2406.12034v2 | 2406.12034 | 10.48550/arXiv.2406.12034 | 23 | 3 | false | null | International Conference on Learning Representations | 0.3451 |
0b6ea7b16a21ead3c53583c915bedd30c36e6dbba760a0203b05fbfaf3d5a2a4 | [
"arxiv",
"semantic_scholar"
] | QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts | Mixture-of-Experts (MoE) is a promising way to scale up the learning capacity of large language models. It increases the number of parameters while keeping FLOPs nearly constant during inference through sparse activation. Yet, it still suffers from significant memory overheads due to the vast parameter size, necessitat... | [
"Pingzhi Li",
"Xiaolong Jin",
"Zhen Tan",
"Yu Cheng",
"Tianlong Chen"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2024-06-12T00:00:00 | https://arxiv.org/abs/2406.08155 | https://arxiv.org/pdf/2406.08155v2 | 2406.08155 | null | 6 | 0 | true | https://github.com/UNITES-Lab/moe-quantization | null | 0.2113 |
5f2dde92f83da20fa72e549d911848d7f257cc09bf738e9c6341e0e0acab3b44 | [
"arxiv",
"semantic_scholar"
] | MoE Jetpack: From Dense Checkpoints to Adaptive Mixture of Experts for Vision Tasks | The sparsely activated mixture of experts (MoE) model presents a promising alternative to traditional densely activated (dense) models, enhancing both quality and computational efficiency. However, training MoE models from scratch demands extensive data and computational resources. Moreover, public repositories like ti... | [
"Xingkui Zhu",
"Yiran Guan",
"Dingkang Liang",
"Yuchao Chen",
"Yuliang Liu",
"Xiang Bai"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-06-07T00:00:00 | https://arxiv.org/abs/2406.04801 | https://arxiv.org/pdf/2406.04801v1 | 2406.04801 | 10.48550/arXiv.2406.04801 | 13 | 0 | true | https://github.com/Adlith/MoE-Jetpack | Neural Information Processing Systems | 0.2865 |
297a438584c7419ab4c88b7d87ed762a3830dea24feabb5e63b2cd8b641dc324 | [
"arxiv",
"semantic_scholar"
] | Skywork-MoE: A Deep Dive into Training Techniques for Mixture-of-Experts Language Models | In this technical report, we introduce the training methodologies implemented in the development of Skywork-MoE, a high-performance mixture-of-experts (MoE) large language model (LLM) with 146 billion parameters and 16 experts. It is initialized from the pre-existing dense checkpoints of our Skywork-13B model. We explo... | [
"Tianwen Wei",
"Bo Zhu",
"Liang Zhao",
"Cheng Cheng",
"Biye Li",
"Weiwei Lü",
"Peng Cheng",
"Jianhao Zhang",
"Xiaoyu Zhang",
"Liang Zeng",
"Xiaokun Wang",
"Yutuan Ma",
"Rui Hu",
"Shuicheng Yan",
"Han Fang",
"Yahui Zhou"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-06-03T00:00:00 | https://arxiv.org/abs/2406.06563 | https://arxiv.org/pdf/2406.06563v1 | 2406.06563 | 10.48550/arXiv.2406.06563 | 60 | 5 | false | null | arXiv.org | 0.4463 |
d2455165de97ad49098297f3306a4e462e3dc04e2b1bafa6c1172867e7f19df8 | [
"arxiv",
"semantic_scholar"
] | MoNDE: Mixture of Near-Data Experts for Large-Scale Sparse Models | Mixture-of-Experts (MoE) large language models (LLM) have memory requirements that often exceed the GPU memory capacity, requiring costly parameter movement from secondary memories to the GPU for expert computation. In this work, we present Mixture of Near-Data Experts (MoNDE), a near-data computing solution that effic... | [
"Taehyun Kim",
"Kwanseok Choi",
"Youngmock Cho",
"Jaehoon Cho",
"Hyuk-Jae Lee",
"Jaewoong Sim"
] | [
"cs.LG",
"cs.AI",
"cs.AR"
] | [
"Computer Science"
] | 2024-05-29T00:00:00 | https://arxiv.org/abs/2405.18832 | https://arxiv.org/pdf/2405.18832v1 | 2405.18832 | 10.1145/3649329.3655951 | 14 | 2 | false | null | Design Automation Conference | 0.294 |
1fde663289f2242784dc1fed3ea3ad7ec151c113005e71fdba2784b73562a300 | [
"arxiv",
"semantic_scholar"
] | MEMoE: Enhancing Model Editing with Mixture of Experts Adaptors | Model editing aims to efficiently alter the behavior of Large Language Models (LLMs) within a desired scope, while ensuring no adverse impact on other inputs. Recent years have witnessed various model editing methods been proposed. However, these methods either exhibit poor overall performance or struggle to strike a b... | [
"Renzhi Wang",
"Piji Li"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-05-29T00:00:00 | https://arxiv.org/abs/2405.19086 | https://arxiv.org/pdf/2405.19086v2 | 2405.19086 | 10.48550/arXiv.2405.19086 | 8 | 1 | false | null | arXiv.org | 0.2386 |
7ca1fd470586f8873d86418af3b2ee803c4293914f583687167c103466f1fd00 | [
"arxiv",
"semantic_scholar"
] | A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts | The sparsely gated mixture of experts (MoE) architecture sends different inputs to different subnetworks, i.e., experts, through trainable routers. MoE reduces the training computation significantly for large models, but its deployment can be still memory or computation expensive for some downstream tasks. Model prunin... | [
"Mohammed Nowaz Rabbani Chowdhury",
"Meng Wang",
"Kaoutar El Maghraoui",
"Naigang Wang",
"Pin-Yu Chen",
"Christopher Carothers"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-05-26T00:00:00 | https://arxiv.org/abs/2405.16646 | https://arxiv.org/pdf/2405.16646v3 | 2405.16646 | 10.48550/arXiv.2405.16646 | 21 | 0 | false | null | International Conference on Machine Learning | 0.3356 |
38bc669a9e5f2deaeeac81b5ac515ae6b1939473b1611f126788e766c035e8e1 | [
"arxiv",
"semantic_scholar"
] | Expert-Token Resonance MoE: Bidirectional Routing with Efficiency Affinity-Driven Active Selection | Mixture-of-Experts (MoE) architectures enable efficient scaling of large language models by activating only a subset of parameters per input. However, existing MoE models suffer from two critical limitations: (1) inefficient token-to-expert routing that causes excessive communication overhead, and (2) expert homogeniza... | [
"Jing Li",
"Zhijie Sun",
"Dachao Lin",
"Xuan He",
"Binfan Zheng",
"Yi Lin",
"Rongqian Zhao",
"Xin Chen"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-05-24T00:00:00 | https://arxiv.org/abs/2406.00023 | https://arxiv.org/pdf/2406.00023v4 | 2406.00023 | null | 2 | 0 | false | null | null | 0.1193 |
c6212034bd43735942ea322bff53b9b655ae8496c5ae2ee4cad64ba091c11d5e | [
"arxiv",
"semantic_scholar"
] | Unchosen Experts Can Contribute Too: Unleashing MoE Models' Power by Self-Contrast | Mixture-of-Experts (MoE) has emerged as a prominent architecture for scaling model size while maintaining computational efficiency. In MoE, each token in the input sequence activates a different subset of experts determined by a routing mechanism. However, the unchosen experts in MoE models do not contribute to the out... | [
"Chufan Shi",
"Cheng Yang",
"Xinyu Zhu",
"Jiahao Wang",
"Taiqiang Wu",
"Siheng Li",
"Deng Cai",
"Yujiu Yang",
"Yu Meng"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2024-05-23T00:00:00 | https://arxiv.org/abs/2405.14507 | https://arxiv.org/pdf/2405.14507v2 | 2405.14507 | 10.48550/arXiv.2405.14507 | 25 | 2 | false | null | Neural Information Processing Systems | 0.3537 |
50713241b14d7debcebd2efcd720f90f716ed8338d182f1ff57360668ab7384b | [
"arxiv",
"semantic_scholar"
] | Revisiting MoE and Dense Speed-Accuracy Comparisons for LLM Training | Mixture-of-Experts (MoE) enjoys performance gain by increasing model capacity while keeping computation cost constant. When comparing MoE to dense models, prior work typically adopt the following setting: 1) use FLOPs or activated parameters as a measure of model complexity; 2) train all models to the same number of to... | [
"Xianzhi Du",
"Tom Gunter",
"Xiang Kong",
"Mark Lee",
"Zirui Wang",
"Aonan Zhang",
"Nan Du",
"Ruoming Pang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-05-23T00:00:00 | https://arxiv.org/abs/2405.15052 | https://arxiv.org/pdf/2405.15052v2 | 2405.15052 | 10.48550/arXiv.2405.15052 | 8 | 0 | true | https://github.com/apple/axlearn} | arXiv.org | 0.2386 |
b428310811cfe6eb56efcb8a86ee2338fa30579a149640f548fc3c9d21d213bf | [
"arxiv",
"semantic_scholar"
] | Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts | Recent advancements in Multimodal Large Language Models (MLLMs) underscore the significance of scalable models and data to boost performance, yet this often incurs substantial computational costs. Although the Mixture of Experts (MoE) architecture has been employed to efficiently scale large language and image-text mod... | [
"Yunxin Li",
"Shenyuan Jiang",
"Baotian Hu",
"Longyue Wang",
"Wanqi Zhong",
"Wenhan Luo",
"Lin Ma",
"Min Zhang"
] | [
"cs.AI",
"cs.CL",
"cs.CV",
"cs.MM"
] | [
"Computer Science",
"Medicine"
] | 2024-05-18T00:00:00 | https://arxiv.org/abs/2405.11273 | https://arxiv.org/pdf/2405.11273v1 | 2405.11273 | 10.1109/TPAMI.2025.3532688 | 144 | 4 | true | https://github.com/HITsz-TMG/UMOE-Scaling-Unified-Multimodal-LLMs | IEEE Transactions on Pattern Analysis and Machine Intelligence | 0.5403 |
5beb2545a623914f7e90d57fc4811bb56190ef6ea0ca9d9e2f55045a33a730c6 | [
"arxiv",
"semantic_scholar"
] | A Mixture of Experts (MoE) model to improve AI-based computational pathology prediction performance under variable levels of histopathology image blur | AI-based models for histopathology whole slide image (WSI) analysis are increasingly common, but unsharp or blurred areas within WSI can significantly reduce prediction performance. In this study, we investigated the effect of image blur on deep learning models and introduced a mixture of experts (MoE) strategy that co... | [
"Yujie Xiang",
"Bojing Liu",
"Mattias Rantalainen"
] | [
"eess.IV",
"cs.CV"
] | [
"Computer Science",
"Engineering"
] | 2024-05-15T00:00:00 | https://arxiv.org/abs/2405.09298 | https://arxiv.org/pdf/2405.09298v5 | 2405.09298 | 10.48550/arXiv.2405.09298 | 0 | 0 | false | null | arXiv.org | 0 |
249a5a4822bf1c83f6e3d35822b9296d0f5d6f5f82ca9eda481e940af348e43c | [
"arxiv",
"semantic_scholar"
] | Mixture of insighTful Experts (MoTE): The Synergy of Thought Chains and Expert Mixtures in Self-Alignment | As the capabilities of large language models (LLMs) continue to expand, aligning these models with human values remains a significant challenge. Recent studies show that reasoning abilities contribute significantly to model safety, while integrating Mixture-of-Experts (MoE) architectures can further enhance alignment. ... | [
"Zhili Liu",
"Yunhao Gou",
"Kai Chen",
"Lanqing Hong",
"Jiahui Gao",
"Fei Mi",
"Yu Zhang",
"Zhenguo Li",
"Xin Jiang",
"Qun Liu",
"James T. Kwok"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-05-01T00:00:00 | https://arxiv.org/abs/2405.00557 | https://arxiv.org/pdf/2405.00557v5 | 2405.00557 | 10.18653/v1/2025.acl-long.151 | 13 | 0 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.2865 |
9d2a1b65e55b45250e85efbfedce65ff9270ab3f71a8e3b6d9f041987912b2c5 | [
"arxiv",
"semantic_scholar"
] | Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models | Recent advancements in general-purpose or domain-specific multimodal large language models (LLMs) have witnessed remarkable progress for medical decision-making. However, they are designated for specific classification or generative tasks, and require model training or finetuning on large-scale datasets with sizeable p... | [
"Songtao Jiang",
"Tuo Zheng",
"Yan Zhang",
"Yeying Jin",
"Li Yuan",
"Zuozhu Liu"
] | [
"cs.CV",
"cs.CL"
] | [
"Computer Science"
] | 2024-04-16T00:00:00 | https://arxiv.org/abs/2404.10237 | https://arxiv.org/pdf/2404.10237v3 | 2404.10237 | 10.18653/v1/2024.findings-emnlp.221 | 86 | 2 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.4849 |
14751e718a4121bb245eca5ade0621839471a21a6006ea8485a7efbc8e34589c | [
"arxiv",
"semantic_scholar"
] | MING-MOE: Enhancing Medical Multi-Task Learning in Large Language Models with Sparse Mixture of Low-Rank Adapter Experts | Large language models like ChatGPT have shown substantial progress in natural language understanding and generation, proving valuable across various disciplines, including the medical field. Despite advancements, challenges persist due to the complexity and diversity inherent in medical tasks which often require multi-... | [
"Yusheng Liao",
"Shuyang Jiang",
"Yu Wang",
"Yanfeng Wang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-04-13T00:00:00 | https://arxiv.org/abs/2404.09027 | https://arxiv.org/pdf/2404.09027v1 | 2404.09027 | 10.48550/arXiv.2404.09027 | 14 | 0 | false | null | arXiv.org | 0.294 |
c2836682c1cea45bb1d3fa93d741ccfe22a30e0855eeef674fafddfb0cf040bd | [
"arxiv",
"semantic_scholar"
] | MoE-FFD: Mixture of Experts for Generalized and Parameter-Efficient Face Forgery Detection | Deepfakes have recently raised significant trust issues and security concerns among the public. Compared to CNN face forgery detectors, ViT-based methods take advantage of the expressivity of transformers, achieving superior detection performance. However, these approaches still exhibit the following limitations: (1) F... | [
"Chenqi Kong",
"Anwei Luo",
"Peijun Bao",
"Yi Yu",
"Haoliang Li",
"Zengwei Zheng",
"Shiqi Wang",
"Alex C. Kot"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-04-12T00:00:00 | https://arxiv.org/abs/2404.08452 | https://arxiv.org/pdf/2404.08452v3 | 2404.08452 | 10.1109/TDSC.2025.3604443 | 54 | 6 | true | https://github.com/LoveSiameseCat/MoE-FFD | IEEE Transactions on Dependable and Secure Computing | 0.4351 |
8b0c2b6e0d92e6cf013940776f88e110ecb5fbf7252f2c44a3abebb551e8ef6b | [
"arxiv",
"semantic_scholar"
] | Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models | Mixture-of-Experts (MoE) language models can reduce computational costs by 2-4$\times$ compared to dense models without sacrificing performance, making them more efficient in computation-bounded scenarios. However, MoE models generally require 2-4$\times$ times more parameters to achieve comparable performance to a den... | [
"Bowen Pan",
"Yikang Shen",
"Haokun Liu",
"Mayank Mishra",
"Gaoyuan Zhang",
"Aude Oliva",
"Colin Raffel",
"Rameswar Panda"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2024-04-08T00:00:00 | https://arxiv.org/abs/2404.05567 | https://arxiv.org/pdf/2404.05567v1 | 2404.05567 | 10.48550/arXiv.2404.05567 | 41 | 2 | false | null | arXiv.org | 0.4058 |
07ed2a685b37ac9d04adb203ec78277150b1e46b57c91e0f5cc585c8fac02004 | [
"arxiv",
"semantic_scholar"
] | SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts | The advancement of deep learning has led to the emergence of Mixture-of-Experts (MoEs) models, known for their dynamic allocation of computational resources based on input. Despite their promise, MoEs face challenges, particularly in terms of memory requirements. To address this, our work introduces SEER-MoE, a novel t... | [
"Alexandre Muzio",
"Alex Sun",
"Churan He"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2024-04-07T00:00:00 | https://arxiv.org/abs/2404.05089 | https://arxiv.org/pdf/2404.05089v1 | 2404.05089 | 10.48550/arXiv.2404.05089 | 37 | 6 | false | null | arXiv.org | 0.4225 |
e8281e850740ea260ad1494faff1a9c7d76749c1d20bb89eaa39d5d25df0153a | [
"arxiv",
"semantic_scholar"
] | Generalization Error Analysis for Sparse Mixture-of-Experts: A Preliminary Study | Mixture-of-Experts (MoE) represents an ensemble methodology that amalgamates predictions from several specialized sub-models (referred to as experts). This fusion is accomplished through a router mechanism, dynamically assigning weights to each expert's contribution based on the input data. Conventional MoE mechanisms ... | [
"Jinze Zhao",
"Peihao Wang",
"Zhangyang Wang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-03-26T00:00:00 | https://arxiv.org/abs/2403.17404 | https://arxiv.org/pdf/2403.17404v1 | 2403.17404 | 10.48550/arXiv.2403.17404 | 6 | 0 | false | null | arXiv.org | 0.2113 |
9e0b1d2adcb48c613c434baea6dc308380adb82370d4e00c13540abab36d975d | [
"arxiv",
"semantic_scholar"
] | An Expert is Worth One Token: Synergizing Multiple Expert LLMs as Generalist via Expert Token Routing | We present Expert-Token-Routing, a unified generalist framework that facilitates seamless integration of multiple expert LLMs. Our framework represents expert LLMs as special expert tokens within the vocabulary of a meta LLM. The meta LLM can route to an expert LLM like generating new tokens. Expert-Token-Routing not o... | [
"Ziwei Chai",
"Guoyin Wang",
"Jing Su",
"Tianjie Zhang",
"Xuanwen Huang",
"Xuwu Wang",
"Jingjing Xu",
"Jianbo Yuan",
"Hongxia Yang",
"Fei Wu",
"Yang Yang"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-03-25T00:00:00 | https://arxiv.org/abs/2403.16854 | https://arxiv.org/pdf/2403.16854v3 | 2403.16854 | 10.48550/arXiv.2403.16854 | 11 | 2 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.2698 |
0f214d43fb2fc34d9edb056d4c5322fed4f7d21ed66e5fdb8702bb37bb1a38f3 | [
"arxiv",
"semantic_scholar"
] | Harder Tasks Need More Experts: Dynamic Routing in MoE Models | In this paper, we introduce a novel dynamic expert selection framework for Mixture of Experts (MoE) models, aiming to enhance computational efficiency and model performance by adjusting the number of activated experts based on input difficulty. Unlike traditional MoE approaches that rely on fixed Top-K routing, which a... | [
"Quzhe Huang",
"Zhenwei An",
"Nan Zhuang",
"Mingxu Tao",
"Chen Zhang",
"Yang Jin",
"Kun Xu",
"Kun Xu",
"Liwei Chen",
"Songfang Huang",
"Yansong Feng"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2024-03-12T00:00:00 | https://arxiv.org/abs/2403.07652 | https://arxiv.org/pdf/2403.07652v1 | 2403.07652 | 10.48550/arXiv.2403.07652 | 80 | 7 | true | https://github.com/ZhenweiAn/Dynamic_MoE | arXiv.org | 0.4771 |
e08f34e22377259558561a7d1968c7e2540af6f7b9655277e6daf2ba0b4f19c1 | [
"arxiv",
"semantic_scholar"
] | Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM | We investigate efficient methods for training Large Language Models (LLMs) to possess capabilities in multiple specialized domains, such as coding, math reasoning and world knowledge. Our method, named Branch-Train-MiX (BTX), starts from a seed model, which is branched to train experts in embarrassingly parallel fashio... | [
"Sainbayar Sukhbaatar",
"Olga Golovneva",
"Vasu Sharma",
"Hu Xu",
"Xi Victoria Lin",
"Baptiste Rozière",
"Jacob Kahn",
"Daniel Li",
"Wen-tau Yih",
"Jason Weston",
"Xian Li"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2024-03-12T00:00:00 | https://arxiv.org/abs/2403.07816 | https://arxiv.org/pdf/2403.07816v1 | 2403.07816 | 10.48550/arXiv.2403.07816 | 109 | 14 | false | null | arXiv.org | 0.588 |
d18f8774c918ca823c13e83395854647d6123fc17c62a0c093317c9d7160a07b | [
"arxiv",
"semantic_scholar"
] | Video Relationship Detection Using Mixture of Experts | Machine comprehension of visual information from images and videos by neural networks faces two primary challenges. Firstly, there exists a computational and inference gap in connecting vision and language, making it difficult to accurately determine which object a given agent acts on and represent it through language.... | [
"Ala Shaabana",
"Zahra Gharaee",
"Paul Fieguth"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2024-03-06T00:00:00 | https://arxiv.org/abs/2403.03994 | https://arxiv.org/pdf/2403.03994v1 | 2403.03994 | 10.1109/ACCESS.2023.3257280 | 6 | 0 | false | null | IEEE Access | 0.2113 |
4db19b656ad2afffb9b54b9fecf50e80ad7426ead3a98a899e9ecd22abe2ffc4 | [
"arxiv",
"semantic_scholar"
] | Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models | A pivotal advancement in the progress of large language models (LLMs) is the emergence of the Mixture-of-Experts (MoE) LLMs. Compared to traditional LLMs, MoE LLMs can achieve higher performance with fewer parameters, but it is still hard to deploy them due to their immense parameter sizes. Different from previous weig... | [
"Xudong Lu",
"Qi Liu",
"Yuhui Xu",
"Aojun Zhou",
"Siyuan Huang",
"Bo Zhang",
"Junchi Yan",
"Hongsheng Li"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-02-22T00:00:00 | https://arxiv.org/abs/2402.14800 | https://arxiv.org/pdf/2402.14800v2 | 2402.14800 | 10.48550/arXiv.2402.14800 | 122 | 24 | true | https://github.com/Lucky-Lance/Expert_Sparsity | Annual Meeting of the Association for Computational Linguistics | 0.699 |
b95e1e4f0764d04c328d09924217651b2df92b3dcdb5c309d0cbc3a8cbb110c6 | [
"arxiv",
"semantic_scholar"
] | HyperMoE: Towards Better Mixture of Experts via Transferring Among Experts | The Mixture of Experts (MoE) for language models has been proven effective in augmenting the capacity of models by dynamically routing each input token to a specific subset of experts for processing. Despite the success, most existing methods face a challenge for balance between sparsity and the availability of expert ... | [
"Hao Zhao",
"Zihan Qiu",
"Huijia Wu",
"Zili Wang",
"Zhaofeng He",
"Jie Fu"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-02-20T00:00:00 | https://arxiv.org/abs/2402.12656 | https://arxiv.org/pdf/2402.12656v4 | 2402.12656 | 10.18653/v1/2024.acl-long.571 | 28 | 0 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.3656 |
8bf3cfeebdf26f6f1a0cd2563ab131292a6bd4889d9f964de107cc2539ad3c62 | [
"arxiv",
"semantic_scholar"
] | Multilinear Mixture of Experts: Scalable Expert Specialization through Factorization | The Mixture of Experts (MoE) paradigm provides a powerful way to decompose dense layers into smaller, modular computations often more amenable to human interpretation, debugging, and editability. However, a major challenge lies in the computational cost of scaling the number of experts high enough to achieve fine-grain... | [
"James Oldfield",
"Markos Georgopoulos",
"Grigorios G. Chrysos",
"Christos Tzelepis",
"Yannis Panagakis",
"Mihalis A. Nicolaou",
"Jiankang Deng",
"Ioannis Patras"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2024-02-19T00:00:00 | https://arxiv.org/abs/2402.12550 | https://arxiv.org/pdf/2402.12550v4 | 2402.12550 | 10.48550/arXiv.2402.12550 | 24 | 1 | true | https://github.com/james-oldfield/muMoE | Neural Information Processing Systems | 0.3495 |
5af02393c8be4a6b9ce9fa23e226443fbfb43b18d8702ae5f3c3d35a21df25c0 | [
"arxiv",
"semantic_scholar"
] | Buffer Overflow in Mixture of Experts | Mixture of Experts (MoE) has become a key ingredient for scaling large foundation models while keeping inference costs steady. We show that expert routing strategies that have cross-batch dependencies are vulnerable to attacks. Malicious queries can be sent to a model and can affect a model's output on other benign que... | [
"Jamie Hayes",
"Ilia Shumailov",
"Itay Yona"
] | [
"cs.CR",
"cs.LG"
] | [
"Computer Science"
] | 2024-02-08T00:00:00 | https://arxiv.org/abs/2402.05526 | https://arxiv.org/pdf/2402.05526v1 | 2402.05526 | 10.48550/arXiv.2402.05526 | 14 | 1 | false | null | arXiv.org | 0.294 |
50325cdf0492ef38b2fbe7f3113cc479d7bd565598de7645ff51b6bd1544193b | [
"arxiv",
"semantic_scholar"
] | Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts | Mixture-of-Experts (MoEs) can scale up beyond traditional deep learning models by employing a routing strategy in which each input is processed by a single "expert" deep learning model. This strategy allows us to scale up the number of parameters defining the MoE while maintaining sparse activation, i.e., MoEs only loa... | [
"Anastasis Kratsios",
"Haitz Sáez de Ocáriz Borde",
"Takashi Furuya",
"Marc T. Law"
] | [
"stat.ML",
"cs.LG",
"cs.NE",
"math.CO",
"math.NA"
] | [
"Mathematics",
"Computer Science"
] | 2024-02-05T00:00:00 | https://arxiv.org/abs/2402.03460 | https://arxiv.org/pdf/2402.03460v2 | 2402.03460 | null | 1 | 0 | false | null | null | 0.0753 |
5743f4db6d90ea691ed873ad6999b7d2f6a2e34ae57b65103b6341aaf52670a2 | [
"arxiv",
"semantic_scholar"
] | CompeteSMoE -- Effective Training of Sparse Mixture of Experts via Competition | Sparse mixture of experts (SMoE) offers an appealing solution to scale up the model complexity beyond the mean of increasing the network's depth or width. However, effective training of SMoE has proven to be challenging due to the representation collapse issue, which causes parameter redundancy and limited representati... | [
"Quang Pham",
"Giang Do",
"Huy Nguyen",
"TrungTin Nguyen",
"Chenghao Liu",
"Mina Sartipi",
"Binh T. Nguyen",
"Savitha Ramasamy",
"Xiaoli Li",
"Steven Hoi",
"Nhat Ho"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-02-04T00:00:00 | https://arxiv.org/abs/2402.02526 | https://arxiv.org/pdf/2402.02526v1 | 2402.02526 | 10.48550/arXiv.2402.02526 | 24 | 2 | false | null | arXiv.org | 0.3495 |
a99b5a50e13d8a4db9632cbb45e7b5ff79f3afd643ec6cfa4a99530087d7d4a1 | [
"arxiv",
"semantic_scholar"
] | MoDE: A Mixture-of-Experts Model with Mutual Distillation among the Experts | The application of mixture-of-experts (MoE) is gaining popularity due to its ability to improve model's performance. In an MoE structure, the gate layer plays a significant role in distinguishing and routing input features to different experts. This enables each expert to specialize in processing their corresponding su... | [
"Zhitian Xie",
"Yinger Zhang",
"Chenyi Zhuang",
"Qitao Shi",
"Zhining Liu",
"Jinjie Gu",
"Guannan Zhang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-01-31T00:00:00 | https://arxiv.org/abs/2402.00893 | https://arxiv.org/pdf/2402.00893v1 | 2402.00893 | 10.48550/arXiv.2402.00893 | 23 | 1 | false | null | AAAI Conference on Artificial Intelligence | 0.3451 |
bb0e4b0f8cd968c7cc2526c5df3301bef928e173bd78344a47bd59f25d9fa23b | [
"arxiv",
"semantic_scholar"
] | LLaVA-MoLE: Sparse Mixture of LoRA Experts for Mitigating Data Conflicts in Instruction Finetuning MLLMs | Instruction finetuning on a variety of image-text instruction data is the key to obtaining a versatile Multimodal Large Language Model (MLLM), and different configurations of the instruction data can lead to finetuned models with different capabilities. However, we have discovered that data conflicts are inevitable whe... | [
"Shaoxiang Chen",
"Zequn Jie",
"Lin Ma"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-01-29T00:00:00 | https://arxiv.org/abs/2401.16160 | https://arxiv.org/pdf/2401.16160v2 | 2401.16160 | 10.48550/arXiv.2401.16160 | 101 | 7 | false | null | arXiv.org | 0.5022 |
acc03edc62e4e3ab3d6201aae2d831a0fcf0a46b8b9cc96db8f35527091982ec | [
"arxiv",
"semantic_scholar"
] | Routers in Vision Mixture of Experts: An Empirical Study | Mixture-of-Experts (MoE) models are a promising way to scale up model capacity without significantly increasing computational cost. A key component of MoEs is the router, which decides which subset of parameters (experts) process which feature embeddings (tokens). In this paper, we present a comprehensive study of rout... | [
"Tianlin Liu",
"Mathieu Blondel",
"Carlos Riquelme",
"Joan Puigcerver"
] | [
"cs.CV",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-01-29T00:00:00 | https://arxiv.org/abs/2401.15969 | https://arxiv.org/pdf/2401.15969v2 | 2401.15969 | 10.48550/arXiv.2401.15969 | 21 | 0 | false | null | null | 0.3356 |
ff734668a04e79abf0cfb182d91fe1a7d94ec87c8718aa80717badfb0ce8f09f | [
"arxiv",
"semantic_scholar"
] | MoE-LLaVA: Mixture of Experts for Large Vision-Language Models | Recent advances demonstrate that scaling Large Vision-Language Models (LVLMs) effectively improves downstream task performances. However, existing scaling methods enable all model parameters to be active for each token in the calculation, which brings massive training and inferring costs. In this work, we propose a sim... | [
"Bin Lin",
"Zhenyu Tang",
"Yang Ye",
"Jinfa Huang",
"Junwu Zhang",
"Yatian Pang",
"Peng Jin",
"Munan Ning",
"Jiebo Luo",
"Li Yuan"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-01-29T00:00:00 | https://arxiv.org/abs/2401.15947 | https://arxiv.org/pdf/2401.15947v5 | 2401.15947 | 10.1109/TMM.2026.3654458 | 337 | 35 | true | https://github.com/PKU-YuanGroup/MoE-LLaVA | IEEE transactions on multimedia | 0.7782 |
0dceb431468b5b3d9d41021398602156e3e63f86cc3355adb0f6ee8097442ad6 | [
"arxiv",
"semantic_scholar"
] | MoE-Infinity: Efficient MoE Inference on Personal Machines with Sparsity-Aware Expert Cache | This paper presents MoE-Infinity, an efficient MoE inference system designed for personal machines with limited GPU memory capacity. The key idea for MoE-Infinity is that on personal machines, which are often single-user environments, MoE-based LLMs typically operate with a batch size of one. In this setting, MoE model... | [
"Leyang Xue",
"Yao Fu",
"Zhan Lu",
"Luo Mai",
"Mahesh Marina"
] | [
"cs.LG",
"cs.PF"
] | [
"Computer Science"
] | 2024-01-25T00:00:00 | https://arxiv.org/abs/2401.14361 | https://arxiv.org/pdf/2401.14361v3 | 2401.14361 | null | 16 | 4 | true | https://github.com/EfficientMoE/MoE-Infinity | null | 0.3495 |
e29183fbae54a78080d5e81f37bbab99a6d33b4eb279c1d4081e723f2ab10c81 | [
"arxiv",
"semantic_scholar"
] | Is Temperature Sample Efficient for Softmax Gaussian Mixture of Experts? | Dense-to-sparse gating mixture of experts (MoE) has recently become an effective alternative to a well-known sparse MoE. Rather than fixing the number of activated experts as in the latter model, which could limit the investigation of potential experts, the former model utilizes the temperature to control the softmax w... | [
"Huy Nguyen",
"Pedram Akbarian",
"Nhat Ho"
] | [
"stat.ML",
"cs.LG"
] | [
"Computer Science",
"Mathematics"
] | 2024-01-25T00:00:00 | https://arxiv.org/abs/2401.13875 | https://arxiv.org/pdf/2401.13875v2 | 2401.13875 | 10.48550/arXiv.2401.13875 | 20 | 0 | false | null | International Conference on Machine Learning | 0.3306 |
9cf88d2e677dc5703a2b3e4ae68cd63cd81c016e73d4abf896f4244132e1989d | [
"arxiv",
"semantic_scholar"
] | Exploiting Inter-Layer Expert Affinity for Accelerating Mixture-of-Experts Model Inference | In large language models like the Generative Pre-trained Transformer, the Mixture of Experts paradigm has emerged as a powerful technique for enhancing model expressiveness and accuracy. However, deploying GPT MoE models for parallel inference on distributed systems presents significant challenges, primarily due to the... | [
"Jinghan Yao",
"Quentin Anthony",
"Aamir Shafi",
"Hari Subramoni",
"Dhabaleswar K.",
" Panda"
] | [
"cs.LG",
"cs.AI",
"cs.DC"
] | [
"Computer Science"
] | 2024-01-16T00:00:00 | https://arxiv.org/abs/2401.08383 | https://arxiv.org/pdf/2401.08383v2 | 2401.08383 | 10.1109/IPDPS57955.2024.00086 | 40 | 7 | false | null | IEEE International Parallel and Distributed Processing Symposium | 0.4515 |
1f701b3e1bae9ceb6e22d474cecdc14f9db106aeef4a020a446439ab73050647 | [
"arxiv",
"semantic_scholar"
] | MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts | State Space Models (SSMs) have become serious contenders in the field of sequential modeling, challenging the dominance of Transformers. At the same time, Mixture of Experts (MoE) has significantly improved Transformer-based Large Language Models, including recent state-of-the-art open models. We propose that to unlock... | [
"Maciej Pióro",
"Kamil Ciebiera",
"Krystian Król",
"Jan Ludziejewski",
"Michał Krutul",
"Jakub Krajewski",
"Szymon Antoniak",
"Piotr Miłoś",
"Marek Cygan",
"Sebastian Jaszczur"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2024-01-08T00:00:00 | https://arxiv.org/abs/2401.04081 | https://arxiv.org/pdf/2401.04081v2 | 2401.04081 | 10.48550/arXiv.2401.04081 | 97 | 5 | false | null | arXiv.org | 0.4978 |
10590fa10041f5b9dfcd5170653fdfa75fb8d9849d644369b769da63bb7e58d2 | [
"arxiv",
"semantic_scholar"
] | Mixtral of Experts | We introduce Mixtral 8x7B, a Sparse Mixture of Experts (SMoE) language model. Mixtral has the same architecture as Mistral 7B, with the difference that each layer is composed of 8 feedforward blocks (i.e. experts). For every token, at each layer, a router network selects two experts to process the current state and com... | [
"Albert Q. Jiang",
"Alexandre Sablayrolles",
"Antoine Roux",
"Arthur Mensch",
"Blanche Savary",
"Chris Bamford",
"Devendra Singh Chaplot",
"Diego de las Casas",
"Emma Bou Hanna",
"Florian Bressand",
"Gianna Lengyel",
"Guillaume Bour",
"Guillaume Lample",
"Lélio Renard Lavaud",
"Lucile Sa... | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2024-01-08T00:00:00 | https://arxiv.org/abs/2401.04088 | https://arxiv.org/pdf/2401.04088v1 | 2401.04088 | 10.48550/arXiv.2401.04088 | 1,950 | 189 | false | null | arXiv.org | 1 |
7f0bcbde324fa4f59cc67328e56cee937f1be0c45c253fbab0ae5652a743c429 | [
"arxiv",
"semantic_scholar"
] | HyperRouter: Towards Efficient Training and Inference of Sparse Mixture of Experts | By routing input tokens to only a few split experts, Sparse Mixture-of-Experts has enabled efficient training of large language models. Recent findings suggest that fixing the routers can achieve competitive performance by alleviating the collapsing problem, where all experts eventually learn similar representations. H... | [
"Giang Do",
"Khiem Le",
"Quang Pham",
"TrungTin Nguyen",
"Thanh-Nam Doan",
"Bint T. Nguyen",
"Chenghao Liu",
"Savitha Ramasamy",
"Xiaoli Li",
"Steven Hoi"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2023-12-12T00:00:00 | https://arxiv.org/abs/2312.07035 | https://arxiv.org/pdf/2312.07035v1 | 2312.07035 | 10.18653/v1/2023.emnlp-main.351 | 26 | 6 | true | https://github.com/giangdip2410/HyperRouter}} | Conference on Empirical Methods in Natural Language Processing | 0.4225 |
daa8a223eaef66cc9a9ceb2c06f422c4d722122e137e9e284f4b52ee0c9278e4 | [
"arxiv",
"semantic_scholar"
] | GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts | Graph data are inherently complex and heterogeneous, leading to a high natural diversity of distributional shifts. However, it remains unclear how to build machine learning architectures that generalize to the complex distributional shifts naturally occurring in the real world. Here, we develop GraphMETRO, a Graph Neur... | [
"Shirley Wu",
"Kaidi Cao",
"Bruno Ribeiro",
"James Zou",
"Jure Leskovec"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2023-12-07T00:00:00 | https://arxiv.org/abs/2312.04693 | https://arxiv.org/pdf/2312.04693v3 | 2312.04693 | 10.52202/079017-0297 | 23 | 2 | true | https://github.com/Wuyxin/GraphMETRO | Neural Information Processing Systems | 0.3451 |
14e3febb9c49f09a6d7c20f2a9af88e6424d757a3c49a4937d5e84ab58f56e4b | [
"arxiv",
"semantic_scholar"
] | Memory Augmented Language Models through Mixture of Word Experts | Scaling up the number of parameters of language models has proven to be an effective approach to improve performance. For dense models, increasing model size proportionally increases the model's computation footprint. In this work, we seek to aggressively decouple learning capacity and FLOPs through Mixture-of-Experts ... | [
"Cicero Nogueira dos Santos",
"James Lee-Thorp",
"Isaac Noble",
"Chung-Ching Chang",
"David Uthus"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2023-11-15T00:00:00 | https://arxiv.org/abs/2311.10768 | https://arxiv.org/pdf/2311.10768v1 | 2311.10768 | 10.48550/arXiv.2311.10768 | 12 | 0 | false | null | North American Chapter of the Association for Computational Linguistics | 0.2785 |
4248e563c80a18759d78f686f64bec6a6419855036d6595eda3c44934e1eb298 | [
"arxiv",
"semantic_scholar"
] | SiDA-MoE: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models | Mixture-of-Experts (MoE) has emerged as a favorable architecture in the era of large models due to its inherent advantage, i.e., enlarging model capacity without incurring notable computational overhead. Yet, the realization of such benefits often results in ineffective GPU memory utilization, as large portions of the ... | [
"Zhixu Du",
"Shiyu Li",
"Yuhao Wu",
"Xiangyu Jiang",
"Jingwei Sun",
"Qilin Zheng",
"Yongkai Wu",
"Ang Li",
"Hai \"Helen\" Li",
"Yiran Chen"
] | [
"cs.LG",
"cs.DC"
] | [
"Computer Science"
] | 2023-10-29T00:00:00 | https://arxiv.org/abs/2310.18859 | https://arxiv.org/pdf/2310.18859v2 | 2310.18859 | 10.48550/arXiv.2310.18859 | 43 | 5 | true | https://github.com/timlee0212/SiDA-MoE | Conference on Machine Learning and Systems | 0.4109 |
c0f206350db0125de66bc11612ccd70946aed5a03699b884fa44432aa4b4683f | [
"arxiv",
"semantic_scholar"
] | Mixture of Tokens: Continuous MoE through Cross-Example Aggregation | Mixture of Experts (MoE) models based on Transformer architecture are pushing the boundaries of language and vision tasks. The allure of these models lies in their ability to substantially increase the parameter count without a corresponding increase in FLOPs. Most widely adopted MoE models are discontinuous with respe... | [
"Szymon Antoniak",
"Michał Krutul",
"Maciej Pióro",
"Jakub Krajewski",
"Jan Ludziejewski",
"Kamil Ciebiera",
"Krystian Król",
"Tomasz Odrzygóźdź",
"Marek Cygan",
"Sebastian Jaszczur"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2023-10-24T00:00:00 | https://arxiv.org/abs/2310.15961 | https://arxiv.org/pdf/2310.15961v2 | 2310.15961 | 10.52202/079017-3300 | 4 | 0 | false | null | Neural Information Processing Systems | 0.1747 |
19997a0c2f918ccbf60b93b2835e75572a886b3f8cd6910f19ccf7ceab774fcd | [
"arxiv",
"semantic_scholar"
] | Merging Experts into One: Improving Computational Efficiency of Mixture of Experts | Scaling the size of language models usually leads to remarkable advancements in NLP tasks. But it often comes with a price of growing computational cost. Although a sparse Mixture of Experts (MoE) can reduce the cost by activating a small subset of parameters (e.g., one expert) for each input, its computation escalates... | [
"Shwai He",
"Run-Ze Fan",
"Liang Ding",
"Li Shen",
"Tianyi Zhou",
"Dacheng Tao"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2023-10-15T00:00:00 | https://arxiv.org/abs/2310.09832 | https://arxiv.org/pdf/2310.09832v3 | 2310.09832 | 10.48550/arXiv.2310.09832 | 44 | 5 | true | https://github.com/Shwai-He/MEO} | Conference on Empirical Methods in Natural Language Processing | 0.4133 |
d5232ec70247029361361f639f48a9f30700001e551e5283c6448a4bbc0fa37a | [
"arxiv",
"semantic_scholar"
] | Sparse Backpropagation for MoE Training | One defining characteristic of Mixture-of-Expert (MoE) models is their capacity for conducting sparse computation via expert routing, leading to remarkable scalability. However, backpropagation, the cornerstone of deep learning, requires dense computation, thereby posting challenges in MoE gradient computations. Here, ... | [
"Liyuan Liu",
"Jianfeng Gao",
"Weizhu Chen"
] | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV"
] | [
"Computer Science"
] | 2023-10-01T00:00:00 | https://arxiv.org/abs/2310.00811 | https://arxiv.org/pdf/2310.00811v1 | 2310.00811 | 10.48550/arXiv.2310.00811 | 18 | 0 | false | null | arXiv.org | 0.3197 |
2a194205da75cc3bdb3065d80bfc364a0aa5f175987d714b989be473f7dfb97d | [
"arxiv",
"semantic_scholar"
] | Statistical Perspective of Top-K Sparse Softmax Gating Mixture of Experts | Top-K sparse softmax gating mixture of experts has been widely used for scaling up massive deep-learning architectures without increasing the computational cost. Despite its popularity in real-world applications, the theoretical understanding of that gating function has remained an open problem. The main challenge come... | [
"Huy Nguyen",
"Pedram Akbarian",
"Fanqi Yan",
"Nhat Ho"
] | [
"stat.ML",
"cs.LG"
] | [
"Computer Science",
"Mathematics"
] | 2023-09-25T00:00:00 | https://arxiv.org/abs/2309.13850 | https://arxiv.org/pdf/2309.13850v2 | 2309.13850 | 10.48550/arXiv.2309.13850 | 31 | 0 | false | null | International Conference on Learning Representations | 0.3763 |
6a460675cb8309e557ef631d6fbdc4e5fb14de7b7f297cb38229d853e5e72748 | [
"arxiv",
"semantic_scholar"
] | Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning | The Mixture of Experts (MoE) is a widely known neural architecture where an ensemble of specialized sub-models optimizes overall performance with a constant computational cost. However, conventional MoEs pose challenges at scale due to the need to store all experts in memory. In this paper, we push MoE to the limit. We... | [
"Ted Zadouri",
"Ahmet Üstün",
"Arash Ahmadian",
"Beyza Ermiş",
"Acyr Locatelli",
"Sara Hooker"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2023-09-11T00:00:00 | https://arxiv.org/abs/2309.05444 | https://arxiv.org/pdf/2309.05444v1 | 2309.05444 | 10.48550/arXiv.2309.05444 | 176 | 19 | true | https://github.com/for-ai/parameter-efficient-moe | International Conference on Learning Representations | 0.6505 |
529a4852a009032b0d81dafd4007176efe81e3ec98696c7220f2f630c8a7a25f | [
"arxiv",
"semantic_scholar"
] | Mobile V-MoEs: Scaling Down Vision Transformers via Sparse Mixture-of-Experts | Sparse Mixture-of-Experts models (MoEs) have recently gained popularity due to their ability to decouple model size from inference efficiency by only activating a small subset of the model parameters for any given input token. As such, sparse MoEs have enabled unprecedented scalability, resulting in tremendous successe... | [
"Erik Daxberger",
"Floris Weers",
"Bowen Zhang",
"Tom Gunter",
"Ruoming Pang",
"Marcin Eichner",
"Michael Emmersberger",
"Yinfei Yang",
"Alexander Toshev",
"Xianzhi Du"
] | [
"cs.CV",
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2023-09-08T00:00:00 | https://arxiv.org/abs/2309.04354 | https://arxiv.org/pdf/2309.04354v1 | 2309.04354 | 10.48550/arXiv.2309.04354 | 19 | 0 | false | null | arXiv.org | 0.3253 |
f4d85c8780a3774cf1d4e2907100bfca389be06ed799ec9912bac09be891b9b0 | [
"arxiv",
"semantic_scholar"
] | Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference | Large language models (LLMs) based on transformers have made significant strides in recent years, the success of which is driven by scaling up their model size. Despite their high algorithmic performance, the computational and memory requirements of LLMs present unprecedented challenges. To tackle the high compute requ... | [
"Ranggi Hwang",
"Jianyu Wei",
"Shijie Cao",
"Changho Hwang",
"Xiaohu Tang",
"Ting Cao",
"Mao Yang"
] | [
"cs.LG",
"cs.AI",
"cs.AR"
] | [
"Computer Science"
] | 2023-08-23T00:00:00 | https://arxiv.org/abs/2308.12066 | https://arxiv.org/pdf/2308.12066v3 | 2308.12066 | 10.1109/ISCA59077.2024.00078 | 126 | 13 | false | null | International Symposium on Computer Architecture | 0.5731 |
0988fa127abfcefd919f5baf9f3941aa8abde4913dafa9d7498e5f2c3b3c42bb | [
"arxiv",
"semantic_scholar"
] | From Sparse to Soft Mixtures of Experts | Sparse mixture of expert architectures (MoEs) scale model capacity without significant increases in training or inference costs. Despite their success, MoEs suffer from a number of issues: training instability, token dropping, inability to scale the number of experts, or ineffective finetuning. In this work, we propose... | [
"Joan Puigcerver",
"Carlos Riquelme",
"Basil Mustafa",
"Neil Houlsby"
] | [
"cs.LG",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2023-08-02T00:00:00 | https://arxiv.org/abs/2308.00951 | https://arxiv.org/pdf/2308.00951v2 | 2308.00951 | 10.48550/arXiv.2308.00951 | 282 | 22 | false | null | International Conference on Learning Representations | 0.6809 |
b6528947169a189fa5c1dbb31b229655d8e3d5153f608d2d4e6dd03debf1be35 | [
"arxiv",
"semantic_scholar"
] | Language-Routing Mixture of Experts for Multilingual and Code-Switching Speech Recognition | Multilingual speech recognition for both monolingual and code-switching speech is a challenging task. Recently, based on the Mixture of Experts (MoE), many works have made good progress in multilingual and code-switching ASR, but present huge computational complexity with the increase of supported languages. In this wo... | [
"Wenxuan Wang",
"Guodong Ma",
"Yuke Li",
"Binbin Du"
] | [
"cs.SD",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2023-07-12T00:00:00 | https://arxiv.org/abs/2307.05956 | https://arxiv.org/pdf/2307.05956v2 | 2307.05956 | 10.48550/arXiv.2307.05956 | 46 | 5 | false | null | Interspeech | 0.418 |
f9e702d107f50de36a9af4dbbab53596e249b084e5b68c6463e9455797bea33b | [
"arxiv",
"semantic_scholar"
] | Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks | In deep learning, mixture-of-experts (MoE) activates one or few experts (sub-networks) on a per-sample or per-token basis, resulting in significant computation reduction. The recently proposed \underline{p}atch-level routing in \underline{MoE} (pMoE) divides each input into $n$ patches (or tokens) and sends $l$ patches... | [
"Mohammed Nowaz Rabbani Chowdhury",
"Shuai Zhang",
"Meng Wang",
"Sijia Liu",
"Pin-Yu Chen"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2023-06-07T00:00:00 | https://arxiv.org/abs/2306.04073 | https://arxiv.org/pdf/2306.04073v1 | 2306.04073 | 10.48550/arXiv.2306.04073 | 47 | 3 | false | null | International Conference on Machine Learning | 0.4203 |
17fece3a8ff526c85ca5c987482d88006b12b4a9b4a6449e04a0611c31206dfd | [
"arxiv",
"semantic_scholar"
] | Soft Merging of Experts with Adaptive Routing | Sparsely activated neural networks with conditional computation learn to route their inputs through different "expert" subnetworks, providing a form of modularity that densely activated models lack. Despite their possible benefits, models with learned routing often underperform their parameter-matched densely activated... | [
"Mohammed Muqeeth",
"Haokun Liu",
"Colin Raffel"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2023-06-06T00:00:00 | https://arxiv.org/abs/2306.03745 | https://arxiv.org/pdf/2306.03745v2 | 2306.03745 | 10.48550/arXiv.2306.03745 | 95 | 8 | false | null | null | 0.4956 |
17f55ed1609682d4c8d2f52d4136145573dc721a552053c6f73931e7a227a991 | [
"arxiv",
"semantic_scholar"
] | LightCurve MoE: A Dynamic Sparse Routing Mixture-of-Experts Architecture for Efficient Stellar Light Curve Classification | The classification of stellar light curves has become a key task in modern time-domain astronomy, fueled by the rapid growth of data from large-scale surveys such as Kepler and TESS. Although deep learning models have achieved high accuracy in this area, their computational costs can limit scalability. To tackle this i... | [
"Cunshi Wang",
"Yu Bai",
"Xinrui Song",
"Jiacheng Xu",
"Henggeng Han",
"Yuyang Li",
"Xinjie Hu",
"Huiqin Yang",
"Jifeng Liu"
] | [
"astro-ph.IM",
"astro-ph.SR"
] | [
"Physics"
] | 2023-05-23T00:00:00 | https://arxiv.org/abs/2305.13745 | https://arxiv.org/pdf/2305.13745v3 | 2305.13745 | 10.1088/1674-4527/adfa73 | 4 | 0 | false | null | null | 0.1747 |
0e298a946d639759e91058921e334dbf9e6e09b73c1e29f9ddc7260202973f39 | [
"arxiv",
"semantic_scholar"
] | Pipeline MoE: A Flexible MoE Implementation with Pipeline Parallelism | The Mixture of Experts (MoE) model becomes an important choice of large language models nowadays because of its scalability with sublinear computational complexity for training and inference. However, existing MoE models suffer from two critical drawbacks, 1) tremendous inner-node and inter-node communication overhead ... | [
"Xin Chen",
"Hengheng Zhang",
"Xiaotao Gu",
"Kaifeng Bi",
"Lingxi Xie",
"Qi Tian"
] | [
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2023-04-22T00:00:00 | https://arxiv.org/abs/2304.11414 | https://arxiv.org/pdf/2304.11414v1 | 2304.11414 | 10.48550/arXiv.2304.11414 | 6 | 0 | false | null | arXiv.org | 0.2113 |
18f4a8a8dcf1f82f9fc257fc967fa23deac54e9c63c8757e1ee21ba5852cca20 | [
"arxiv",
"semantic_scholar"
] | WM-MoE: Weather-aware Multi-scale Mixture-of-Experts for Blind Adverse Weather Removal | Adverse weather removal tasks like deraining, desnowing, and dehazing are usually treated as separate tasks. However, in practical autonomous driving scenarios, the type, intensity,and mixing degree of weather are unknown, so handling each task separately cannot deal with the complex practical scenarios. In this paper,... | [
"Yulin Luo",
"Rui Zhao",
"Xiaobao Wei",
"Jinwei Chen",
"Yijie Lu",
"Shenghao Xie",
"Tianyu Wang",
"Ruiqin Xiong",
"Ming Lu",
"Shanghang Zhang"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2023-03-24T00:00:00 | https://arxiv.org/abs/2303.13739 | https://arxiv.org/pdf/2303.13739v2 | 2303.13739 | null | 10 | 0 | false | null | null | 0.2603 |
d9e5a800e30b28a8996607942ef10e2df89b0c0e3031365210b6b04d9bfd2d9e | [
"arxiv",
"semantic_scholar"
] | Scaling Vision-Language Models with Sparse Mixture of Experts | The field of natural language processing (NLP) has made significant strides in recent years, particularly in the development of large-scale vision-language models (VLMs). These models aim to bridge the gap between text and visual information, enabling a more comprehensive understanding of multimedia data. However, as t... | [
"Sheng Shen",
"Zhewei Yao",
"Chunyuan Li",
"Trevor Darrell",
"Kurt Keutzer",
"Yuxiong He"
] | [
"cs.CV",
"cs.CL"
] | [
"Computer Science"
] | 2023-03-13T00:00:00 | https://arxiv.org/abs/2303.07226 | https://arxiv.org/pdf/2303.07226v1 | 2303.07226 | 10.48550/arXiv.2303.07226 | 116 | 3 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.517 |
812cc422f79d2390ebb082e97378bd2f6cced4fb64910b98506c740e72c5f127 | [
"arxiv",
"semantic_scholar"
] | Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference | Mixture-of-Experts (MoE) models have gained popularity in achieving state-of-the-art performance in a wide range of tasks in computer vision and natural language processing. They effectively expand the model capacity while incurring a minimal increase in computation cost during training. However, deploying such models ... | [
"Haiyang Huang",
"Newsha Ardalani",
"Anna Sun",
"Liu Ke",
"Hsien-Hsin S. Lee",
"Anjali Sridhar",
"Shruti Bhosale",
"Carole-Jean Wu",
"Benjamin Lee"
] | [
"cs.DC",
"cs.AR",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2023-03-10T00:00:00 | https://arxiv.org/abs/2303.06182 | https://arxiv.org/pdf/2303.06182v2 | 2303.06182 | 10.48550/arXiv.2303.06182 | 47 | 8 | false | null | arXiv.org | 0.4771 |
88664b6ec4aee1f109552fba913030581bd317e4f2e6a7f64894d34bd8ec2552 | [
"arxiv",
"semantic_scholar"
] | Improving Expert Specialization in Mixture of Experts | Mixture of experts (MoE), introduced over 20 years ago, is the simplest gated modular neural network architecture. There is renewed interest in MoE because the conditional computation allows only parts of the network to be used during each inference, as was recently demonstrated in large scale natural language processi... | [
"Yamuna Krishnamurthy",
"Chris Watkins",
"Thomas Gaertner"
] | [
"cs.LG",
"cs.AI",
"cs.NE"
] | [
"Computer Science"
] | 2023-02-28T00:00:00 | https://arxiv.org/abs/2302.14703 | https://arxiv.org/pdf/2302.14703v1 | 2302.14703 | 10.48550/arXiv.2302.14703 | 22 | 0 | false | null | arXiv.org | 0.3404 |
d202c846ad5eee7d54bba0e9692bba3c7e8535cecf1c66eedde76a0bb221973b | [
"arxiv",
"semantic_scholar"
] | TA-MoE: Topology-Aware Large Scale Mixture-of-Expert Training | Sparsely gated Mixture-of-Expert (MoE) has demonstrated its effectiveness in scaling up deep neural networks to an extreme scale. Despite that numerous efforts have been made to improve the performance of MoE from the model design or system optimization perspective, existing MoE dispatch patterns are still not able to ... | [
"Chang Chen",
"Min Li",
"Zhihua Wu",
"Dianhai Yu",
"Chao Yang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2023-02-20T00:00:00 | https://arxiv.org/abs/2302.09915 | https://arxiv.org/pdf/2302.09915v1 | 2302.09915 | 10.48550/arXiv.2302.09915 | 27 | 2 | false | null | Neural Information Processing Systems | 0.3618 |
e93f2f5d9ab1192e16b6a67eef786dbffbb477b46073163026e0e0a1eac6308f | [
"arxiv",
"semantic_scholar"
] | SMILE: Scaling Mixture-of-Experts with Efficient Bi-level Routing | The mixture of Expert (MoE) parallelism is a recent advancement that scales up the model size with constant computational cost. MoE selects different sets of parameters (i.e., experts) for each incoming token, resulting in a sparsely-activated model. Despite several successful applications of MoE, its training efficien... | [
"Chaoyang He",
"Shuai Zheng",
"Aston Zhang",
"George Karypis",
"Trishul Chilimbi",
"Mahdi Soltanolkotabi",
"Salman Avestimehr"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2022-12-10T00:00:00 | https://arxiv.org/abs/2212.05191 | https://arxiv.org/pdf/2212.05191v1 | 2212.05191 | 10.48550/arXiv.2212.05191 | 5 | 0 | false | null | arXiv.org | 0.1945 |
fb74f3d655b4e988d1c7f345783ce60352c39ad886d609ac01d7c93c3d261b0f | [
"arxiv",
"semantic_scholar"
] | MegaBlocks: Efficient Sparse Training with Mixture-of-Experts | We present MegaBlocks, a system for efficient Mixture-of-Experts (MoE) training on GPUs. Our system is motivated by the limitations of current frameworks, which restrict the dynamic routing in MoE layers to satisfy the constraints of existing software and hardware. These formulations force a tradeoff between model qual... | [
"Trevor Gale",
"Deepak Narayanan",
"Cliff Young",
"Matei Zaharia"
] | [
"cs.LG",
"cs.AI",
"cs.DC"
] | [
"Computer Science"
] | 2022-11-29T00:00:00 | https://arxiv.org/abs/2211.15841 | https://arxiv.org/pdf/2211.15841v1 | 2211.15841 | 10.48550/arXiv.2211.15841 | 221 | 21 | false | null | Conference on Machine Learning and Systems | 0.6712 |
3ee3889efa32b44d470cad3406ccb16213a6e19caeb1a4c4eb238d76714d3206 | [
"arxiv",
"semantic_scholar"
] | Handling Trade-Offs in Speech Separation with Sparsely-Gated Mixture of Experts | Employing a monaural speech separation (SS) model as a front-end for automatic speech recognition (ASR) involves balancing two kinds of trade-offs. First, while a larger model improves the SS performance, it also requires a higher computational cost. Second, an SS model that is more optimized for handling overlapped sp... | [
"Xiaofei Wang",
"Zhuo Chen",
"Yu Shi",
"Jian Wu",
"Naoyuki Kanda",
"Takuya Yoshioka"
] | [
"eess.AS",
"cs.SD",
"eess.SP"
] | [
"Engineering",
"Computer Science"
] | 2022-11-11T00:00:00 | https://arxiv.org/abs/2211.06493 | https://arxiv.org/pdf/2211.06493v2 | 2211.06493 | null | 2 | 0 | false | null | null | 0.1193 |
023f0bb174237258d52133d7487f5e278cfb03546ae857b44938a4c88312b9b7 | [
"arxiv",
"semantic_scholar"
] | On the Adversarial Robustness of Mixture of Experts | Adversarial robustness is a key desirable property of neural networks. It has been empirically shown to be affected by their sizes, with larger networks being typically more robust. Recently, Bubeck and Sellke proved a lower bound on the Lipschitz constant of functions that fit the training data in terms of their numbe... | [
"Joan Puigcerver",
"Rodolphe Jenatton",
"Carlos Riquelme",
"Pranjal Awasthi",
"Srinadh Bhojanapalli"
] | [
"cs.LG",
"cs.AI",
"cs.CR",
"cs.CV"
] | [
"Computer Science"
] | 2022-10-19T00:00:00 | https://arxiv.org/abs/2210.10253 | https://arxiv.org/pdf/2210.10253v1 | 2210.10253 | 10.48550/arXiv.2210.10253 | 29 | 1 | false | null | Neural Information Processing Systems | 0.3693 |
a4b1d1639c6a61345d5b2f8157bcab5bbe27516fef3175e1a193795f65204cce | [
"arxiv",
"semantic_scholar"
] | Towards Understanding Mixture of Experts in Deep Learning | The Mixture-of-Experts (MoE) layer, a sparsely-activated model controlled by a router, has achieved great success in deep learning. However, the understanding of such architecture remains elusive. In this paper, we formally study how the MoE layer improves the performance of neural network learning and why the mixture ... | [
"Zixiang Chen",
"Yihe Deng",
"Yue Wu",
"Quanquan Gu",
"Yuanzhi Li"
] | [
"cs.LG",
"cs.AI",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2022-08-04T00:00:00 | https://arxiv.org/abs/2208.02813 | https://arxiv.org/pdf/2208.02813v1 | 2208.02813 | 10.48550/arXiv.2208.02813 | 90 | 3 | false | null | arXiv.org | 0.4898 |
f975165a5db65000bdaa39fe2dea19c55931357e72abdf06a2c90d21f68fe909 | [
"arxiv",
"semantic_scholar"
] | MoEC: Mixture of Expert Clusters | Sparsely Mixture of Experts (MoE) has received great interest due to its promising scaling capability with affordable computational overhead. MoE converts dense layers into sparse experts, and utilizes a gated routing network to make experts conditionally activated. However, as the number of experts grows, MoE with out... | [
"Yuan Xie",
"Shaohan Huang",
"Tianyu Chen",
"Furu Wei"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2022-07-19T00:00:00 | https://arxiv.org/abs/2207.09094 | https://arxiv.org/pdf/2207.09094v1 | 2207.09094 | 10.48550/arXiv.2207.09094 | 27 | 0 | false | null | AAAI Conference on Artificial Intelligence | 0.3618 |
c785f0d799b0db5b0286520d8d0b502db5a6d404adf607914853dc2446490cde | [
"arxiv",
"semantic_scholar"
] | Task-Specific Expert Pruning for Sparse Mixture-of-Experts | The sparse Mixture-of-Experts (MoE) model is powerful for large-scale pre-training and has achieved promising results due to its model capacity. However, with trillions of parameters, MoE is hard to be deployed on cloud or mobile environment. The inference of MoE requires expert parallelism, which is not hardware-frien... | [
"Tianyu Chen",
"Shaohan Huang",
"Yuan Xie",
"Binxing Jiao",
"Daxin Jiang",
"Haoyi Zhou",
"Jianxin Li",
"Furu Wei"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2022-06-01T00:00:00 | https://arxiv.org/abs/2206.00277 | https://arxiv.org/pdf/2206.00277v2 | 2206.00277 | 10.48550/arXiv.2206.00277 | 81 | 5 | false | null | arXiv.org | 0.4785 |
b108a3b5051a5417e6fe5c7898d2eab91572f3c49cf0daa98683a282e875cf60 | [
"arxiv",
"semantic_scholar"
] | Sparse Mixers: Combining MoE and Mixing to build a more efficient BERT | We combine the capacity of sparsely gated Mixture-of-Experts (MoE) with the speed and stability of linear, mixing transformations to design the Sparse Mixer encoder model. Sparse Mixer slightly outperforms (<1%) BERT on GLUE and SuperGLUE, but more importantly trains 65% faster and runs inference 61% faster. We also pr... | [
"James Lee-Thorp",
"Joshua Ainslie"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2022-05-24T00:00:00 | https://arxiv.org/abs/2205.12399 | https://arxiv.org/pdf/2205.12399v2 | 2205.12399 | 10.48550/arXiv.2205.12399 | 15 | 0 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.301 |
c914af26731e1c154a1801eccc32dfceee33706f6c12f65361c6babde60f7377 | [
"arxiv",
"semantic_scholar"
] | Sparsely-gated Mixture-of-Expert Layers for CNN Interpretability | Sparsely-gated Mixture of Expert (MoE) layers have been recently successfully applied for scaling large transformers, especially for language modeling tasks. An intriguing side effect of sparse MoE layers is that they convey inherent interpretability to a model via natural expert specialization. In this work, we apply ... | [
"Svetlana Pavlitska",
"Christian Hubschneider",
"Lukas Struppek",
"J. Marius Zöllner"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2022-04-22T00:00:00 | https://arxiv.org/abs/2204.10598 | https://arxiv.org/pdf/2204.10598v3 | 2204.10598 | 10.1109/IJCNN54540.2023.10191904 | 21 | 2 | false | null | IEEE International Joint Conference on Neural Network | 0.3356 |
121e40f91caf82937fb29f7494d54eb4789939a4cc40d349bf445d10481225fd | [
"arxiv",
"semantic_scholar"
] | On the Representation Collapse of Sparse Mixture of Experts | Sparse mixture of experts provides larger model capacity while requiring a constant computational overhead. It employs the routing mechanism to distribute input tokens to the best-matched experts according to their hidden representations. However, learning such a routing mechanism encourages token clustering around exp... | [
"Zewen Chi",
"Li Dong",
"Shaohan Huang",
"Damai Dai",
"Shuming Ma",
"Barun Patra",
"Saksham Singhal",
"Payal Bajaj",
"Xia Song",
"Xian-Ling Mao",
"Heyan Huang",
"Furu Wei"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2022-04-20T00:00:00 | https://arxiv.org/abs/2204.09179 | https://arxiv.org/pdf/2204.09179v3 | 2204.09179 | 10.48550/arXiv.2204.09179 | 188 | 23 | false | null | Neural Information Processing Systems | 0.6901 |
963ffb520cf2c7d94f0deab834e4b2e64ca38ae1001fb253f7bd1e06ff2e5326 | [
"arxiv",
"semantic_scholar"
] | Residual Mixture of Experts | Mixture of Experts (MoE) is able to scale up vision transformers effectively. However, it requires prohibiting computation resources to train a large MoE transformer. In this paper, we propose Residual Mixture of Experts (RMoE), an efficient training pipeline for MoE vision transformers on downstream tasks, such as seg... | [
"Lemeng Wu",
"Mengchen Liu",
"Yinpeng Chen",
"Dongdong Chen",
"Xiyang Dai",
"Lu Yuan"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2022-04-20T00:00:00 | https://arxiv.org/abs/2204.09636 | https://arxiv.org/pdf/2204.09636v3 | 2204.09636 | 10.48550/arXiv.2204.09636 | 54 | 4 | false | null | arXiv.org | 0.4351 |
9bcf711e10fdafbeb24c8e0df8ae51ed860dbcc11a31a92b5c705e597e18ae9f | [
"arxiv",
"semantic_scholar"
] | StableMoE: Stable Routing Strategy for Mixture of Experts | The Mixture-of-Experts (MoE) technique can scale up the model size of Transformers with an affordable computational overhead. We point out that existing learning-to-route MoE methods suffer from the routing fluctuation issue, i.e., the target expert of the same input may change along with training, but only one expert ... | [
"Damai Dai",
"Li Dong",
"Shuming Ma",
"Bo Zheng",
"Zhifang Sui",
"Baobao Chang",
"Furu Wei"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2022-04-18T00:00:00 | https://arxiv.org/abs/2204.08396 | https://arxiv.org/pdf/2204.08396v1 | 2204.08396 | 10.48550/arXiv.2204.08396 | 123 | 8 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.5234 |
717325a1b7a7d890848626432b01bd84d564f2bdc273db4a9359329cb271ed39 | [
"arxiv",
"semantic_scholar"
] | Sparsely Activated Mixture-of-Experts are Robust Multi-Task Learners | Traditional multi-task learning (MTL) methods use dense networks that use the same set of shared weights across several different tasks. This often creates interference where two or more tasks compete to pull model parameters in different directions. In this work, we study whether sparsely activated Mixture-of-Experts ... | [
"Shashank Gupta",
"Subhabrata Mukherjee",
"Krishan Subudhi",
"Eduardo Gonzalez",
"Damien Jose",
"Ahmed H. Awadallah",
"Jianfeng Gao"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2022-04-16T00:00:00 | https://arxiv.org/abs/2204.07689 | https://arxiv.org/pdf/2204.07689v1 | 2204.07689 | 10.48550/arXiv.2204.07689 | 64 | 1 | false | null | arXiv.org | 0.4532 |
c12259d573f08635e2e84281eb5503d30f9baf10c4b5989acd4c155dc6404731 | [
"arxiv",
"semantic_scholar"
] | Mixture of Experts for Biomedical Question Answering | Biomedical Question Answering (BQA) has attracted increasing attention in recent years due to its promising application prospect. It is a challenging task because the biomedical questions are professional and usually vary widely. Existing question answering methods answer all questions with a homogeneous model, leading... | [
"Damai Dai",
"Wenbin Jiang",
"Jiyuan Zhang",
"Weihua Peng",
"Yajuan Lyu",
"Zhifang Sui",
"Baobao Chang",
"Yong Zhu"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2022-04-15T00:00:00 | https://arxiv.org/abs/2204.07469 | https://arxiv.org/pdf/2204.07469v1 | 2204.07469 | 10.48550/arXiv.2204.07469 | 13 | 0 | false | null | Natural Language Processing and Chinese Computing | 0.2865 |
a0898c4a33d8375f6654e6d7924008211d10197295e70848fa2d92f8f3fdbe30 | [
"arxiv",
"semantic_scholar"
] | Build a Robust QA System with Transformer-based Mixture of Experts | In this paper, we aim to build a robust question answering system that can adapt to out-of-domain datasets. A single network may overfit to the superficial correlation in the training distribution, but with a meaningful number of expert sub-networks, a gating network that selects a sparse combination of experts for eac... | [
"Yu Qing Zhou",
"Xixuan Julie Liu",
"Yuanzhe Dong"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2022-03-20T00:00:00 | https://arxiv.org/abs/2204.09598 | https://arxiv.org/pdf/2204.09598v1 | 2204.09598 | 10.48550/arXiv.2204.09598 | 1 | 0 | false | null | arXiv.org | 0.0753 |
c33bfa34eb3a42efdddcdcee04ccdc1c58dd7b9f095e4b90cbde9f500c5c308e | [
"arxiv",
"semantic_scholar"
] | Support Recovery in Mixture Models with Sparse Parameters | Mixture models are widely used to fit complex and multimodal datasets. In this paper we study mixtures with high dimensional sparse latent parameter vectors and consider the problem of support recovery of those vectors. While parameter learning in mixture models is well-studied, the sparsity constraint remains relative... | [
"Arya Mazumdar",
"Soumyabrata Pal"
] | [
"cs.LG",
"cs.IT",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2022-02-24T00:00:00 | https://arxiv.org/abs/2202.11940 | https://arxiv.org/pdf/2202.11940v2 | 2202.11940 | 10.1109/TIT.2024.3462937 | 1 | 0 | false | null | IEEE Transactions on Information Theory | 0.0753 |
6c4668e251cb0da1c5faf5d4eb06f2762f6a78d302ccef6cf9331f1112cfd821 | [
"arxiv",
"semantic_scholar"
] | Mixture-of-Experts with Expert Choice Routing | Sparsely-activated Mixture-of-experts (MoE) models allow the number of parameters to greatly increase while keeping the amount of computation for a given token or a given sample unchanged. However, a poor expert routing strategy (e.g. one resulting in load imbalance) can cause certain experts to be under-trained, leadi... | [
"Yanqi Zhou",
"Tao Lei",
"Hanxiao Liu",
"Nan Du",
"Yanping Huang",
"Vincent Zhao",
"Andrew Dai",
"Zhifeng Chen",
"Quoc Le",
"James Laudon"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2022-02-18T00:00:00 | https://arxiv.org/abs/2202.09368 | https://arxiv.org/pdf/2202.09368v2 | 2202.09368 | 10.52202/068431-0515 | 775 | 43 | false | null | Neural Information Processing Systems | 0.8217 |
2aa3a854199d5839fd2b0d868d14d135b30fa1ced20db558570dfca5e6a6bf7b | [
"arxiv",
"semantic_scholar"
] | ST-MoE: Designing Stable and Transferable Sparse Expert Models | Scale has opened new frontiers in natural language processing -- but at a high cost. In response, Mixture-of-Experts (MoE) and Switch Transformers have been proposed as an energy efficient path to even larger and more capable language models. But advancing the state-of-the-art across a broad set of natural language tas... | [
"Barret Zoph",
"Irwan Bello",
"Sameer Kumar",
"Nan Du",
"Yanping Huang",
"Jeff Dean",
"Noam Shazeer",
"William Fedus"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2022-02-17T00:00:00 | https://arxiv.org/abs/2202.08906 | https://arxiv.org/pdf/2202.08906v2 | 2202.08906 | null | 411 | 26 | false | null | null | 0.7157 |
c6e4164642396f36eed0941b2001f06b4cc8a2fe9eb5144d99f27ceead60c77e | [
"arxiv",
"semantic_scholar"
] | DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale | As the training of giant dense models hits the boundary on the availability and capability of the hardware resources today, Mixture-of-Experts (MoE) models become one of the most promising model architectures due to their significant training cost reduction compared to a quality-equivalent dense model. Its training cos... | [
"Samyam Rajbhandari",
"Conglong Li",
"Zhewei Yao",
"Minjia Zhang",
"Reza Yazdani Aminabadi",
"Ammar Ahmad Awan",
"Jeff Rasley",
"Yuxiong He"
] | [
"cs.LG",
"cs.AI",
"cs.DC"
] | [
"Computer Science"
] | 2022-01-14T00:00:00 | https://arxiv.org/abs/2201.05596 | https://arxiv.org/pdf/2201.05596v2 | 2201.05596 | null | 563 | 58 | false | null | International Conference on Machine Learning | 0.8854 |
f278e5f6b2ca16a750dbc62da96f8f270528fa42bc8222d5446b5aec4445fe22 | [
"arxiv",
"semantic_scholar"
] | EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate | Mixture-of-experts (MoE) is becoming popular due to its success in improving the model quality, especially in Transformers. By routing tokens with a sparse gate to a few experts (i.e., a small pieces of the full model), MoE can easily increase the model parameters to a very large scale while keeping the computation cos... | [
"Xiaonan Nie",
"Xupeng Miao",
"Shijie Cao",
"Lingxiao Ma",
"Qibin Liu",
"Jilong Xue",
"Youshan Miao",
"Yi Liu",
"Zhi Yang",
"Bin Cui"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2021-12-29T00:00:00 | https://arxiv.org/abs/2112.14397 | https://arxiv.org/pdf/2112.14397v2 | 2112.14397 | null | 39 | 4 | false | null | null | 0.4005 |
906ab457f419b7935aadb72a52c65f1b0b467b8a8175115f4d3d5755aad0fe82 | [
"arxiv",
"semantic_scholar"
] | Building a great multi-lingual teacher with sparsely-gated mixture of experts for speech recognition | The sparsely-gated Mixture of Experts (MoE) can magnify a network capacity with a little computational complexity. In this work, we investigate how multi-lingual Automatic Speech Recognition (ASR) networks can be scaled up with a simple routing algorithm in order to achieve better accuracy. More specifically, we apply ... | [
"Kenichi Kumatani",
"Robert Gmyr",
"Felipe Cruz Salinas",
"Linquan Liu",
"Wei Zuo",
"Devang Patel",
"Eric Sun",
"Yu Shi"
] | [
"cs.CL",
"cs.AI",
"cs.LG",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2021-12-10T00:00:00 | https://arxiv.org/abs/2112.05820 | https://arxiv.org/pdf/2112.05820v3 | 2112.05820 | null | 22 | 0 | false | null | arXiv.org | 0.3404 |
58ee66f6ea3c2745c9a01ef1f66ab21d1a2b331b950c593d56c27345a5f997ba | [
"arxiv",
"semantic_scholar"
] | SpeechMoE2: Mixture-of-Experts Model with Improved Routing | Mixture-of-experts based acoustic models with dynamic routing mechanisms have proved promising results for speech recognition. The design principle of router architecture is important for the large model capacity and high computational efficiency. Our previous work SpeechMoE only uses local grapheme embedding to help r... | [
"Zhao You",
"Shulin Feng",
"Dan Su",
"Dong Yu"
] | [
"eess.AS",
"cs.CL",
"cs.SD"
] | [
"Computer Science",
"Engineering"
] | 2021-11-23T00:00:00 | https://arxiv.org/abs/2111.11831 | https://arxiv.org/pdf/2111.11831v1 | 2111.11831 | 10.1109/icassp43922.2022.9747065 | 41 | 1 | false | null | IEEE International Conference on Acoustics, Speech, and Signal Processing | 0.4058 |
c0e84cd18c452c9716ff9052c5643cd465a9e0da8c8ec5d8917a4976cfcf2bec | [
"arxiv",
"semantic_scholar"
] | Taming Sparsely Activated Transformer with Stochastic Experts | Sparsely activated models (SAMs), such as Mixture-of-Experts (MoE), can easily scale to have outrageously large amounts of parameters without significant increase in computational cost. However, SAMs are reported to be parameter inefficient such that larger models do not always lead to better performance. While most on... | [
"Simiao Zuo",
"Xiaodong Liu",
"Jian Jiao",
"Young Jin Kim",
"Hany Hassan",
"Ruofei Zhang",
"Tuo Zhao",
"Jianfeng Gao"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2021-10-08T00:00:00 | https://arxiv.org/abs/2110.04260 | https://arxiv.org/pdf/2110.04260v3 | 2110.04260 | null | 149 | 14 | true | https://github.com/microsoft/Stochastic-Mixture-of-Experts | International Conference on Learning Representations | 0.588 |
b889c7cae9a0b241006e9c9e7f2a330aa07cae85796e4f2e97f03e6ee4f9a98e | [
"arxiv",
"semantic_scholar"
] | Sparse MoEs meet Efficient Ensembles | Machine learning models based on the aggregated outputs of submodels, either at the activation or prediction levels, often exhibit strong performance compared to individual models. We study the interplay of two popular classes of such models: ensembles of neural networks and sparse mixture of experts (sparse MoEs). Fir... | [
"James Urquhart Allingham",
"Florian Wenzel",
"Zelda E Mariet",
"Basil Mustafa",
"Joan Puigcerver",
"Neil Houlsby",
"Ghassen Jerfel",
"Vincent Fortuin",
"Balaji Lakshminarayanan",
"Jasper Snoek",
"Dustin Tran",
"Carlos Riquelme Ruiz",
"Rodolphe Jenatton"
] | [
"cs.LG",
"cs.CV",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2021-10-07T00:00:00 | https://arxiv.org/abs/2110.03360 | https://arxiv.org/pdf/2110.03360v2 | 2110.03360 | null | 24 | 0 | false | null | null | 0.3495 |
58fec9c4a70cac02837664f8342a863cd2f4c725421fd98b76aaabb533354c5f | [
"arxiv",
"semantic_scholar"
] | Scaling Vision with Sparse Mixture of Experts | Sparsely-gated Mixture of Experts networks (MoEs) have demonstrated excellent scalability in Natural Language Processing. In Computer Vision, however, almost all performant networks are "dense", that is, every input is processed by every parameter. We present a Vision MoE (V-MoE), a sparse version of the Vision Transfo... | [
"Carlos Riquelme",
"Joan Puigcerver",
"Basil Mustafa",
"Maxim Neumann",
"Rodolphe Jenatton",
"André Susano Pinto",
"Daniel Keysers",
"Neil Houlsby"
] | [
"cs.CV",
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2021-06-10T00:00:00 | https://arxiv.org/abs/2106.05974 | https://arxiv.org/pdf/2106.05974v1 | 2106.05974 | null | 1,061 | 66 | false | null | Neural Information Processing Systems | 0.913 |
f97ebcc08ebf096aebd868609a3bd1b079fbb9d12f89e0135581500ff90442e4 | [
"arxiv",
"semantic_scholar"
] | M6-T: Exploring Sparse Expert Models and Beyond | Mixture-of-Experts (MoE) models can achieve promising results with outrageous large amount of parameters but constant computation cost, and thus it has become a trend in model scaling. Still it is a mystery how MoE layers bring quality gains by leveraging the parameters with sparse activation. In this work, we investig... | [
"An Yang",
"Junyang Lin",
"Rui Men",
"Chang Zhou",
"Le Jiang",
"Xianyan Jia",
"Ang Wang",
"Jie Zhang",
"Jiamang Wang",
"Yong Li",
"Di Zhang",
"Wei Lin",
"Lin Qu",
"Jingren Zhou",
"Hongxia Yang"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2021-05-31T00:00:00 | https://arxiv.org/abs/2105.15082 | https://arxiv.org/pdf/2105.15082v5 | 2105.15082 | null | 24 | 3 | false | null | arXiv.org | 0.3495 |
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