{"total":15,"rows":[{"id":"2407.06204","title":"A Survey on Mixture of Experts in Large Language Models","created":"2024-06-26","cited_by_count":237,"area":"moe"},{"id":"2206.03382","title":"Tutel: Adaptive Mixture-of-Experts at Scale","created":"2022-06-07","cited_by_count":201,"area":"moe"},{"id":"2110.04260","title":"Taming Sparsely Activated Transformer with Stochastic Experts","created":"2021-10-08","cited_by_count":113,"area":"moe"},{"id":"2107.11817","title":"Go Wider Instead of Deeper","created":"2021-07-25","cited_by_count":57,"area":"moe"},{"id":"2204.07675","title":"MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation","created":"2022-04-15","cited_by_count":37,"area":"moe"},{"id":"2303.06182","title":"Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference","created":"2023-03-10","cited_by_count":37,"area":"moe"},{"id":"2203.14685","title":"HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System","created":"2022-03-28","cited_by_count":36,"area":"moe"},{"id":"2203.01104","title":"Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models","created":"2022-03-02","cited_by_count":31,"area":"moe"},{"id":"2403.18926","title":"XMoE: Sparse Models with Fine-grained and Adaptive Expert Selection","created":"2024-02-27","cited_by_count":28,"area":"moe"},{"id":"2205.14336","title":"Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers","created":"2022-05-28","cited_by_count":22,"area":"moe"},{"id":"2503.06823","title":"eMoE: Task-aware Memory Efficient Mixture-of-Experts-Based (MoE) Model Inference","created":"2025-03-10","cited_by_count":11,"area":"moe"},{"id":"2109.11817","title":"Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts","created":"2021-09-24","cited_by_count":10,"area":"moe"},{"id":"2203.06850","title":"Efficient Language Modeling with Sparse all-MLP","created":"2022-03-14","cited_by_count":8,"area":"moe"},{"id":"2411.08446","title":"LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing","created":"2024-11-13","cited_by_count":5,"area":"moe"},{"id":"2409.14107","title":"Routing in Sparsely-gated Language Models responds to Context","created":"2024-09-21","cited_by_count":1,"area":"moe"}],"next_cursor":null}