{"total":67,"rows":[{"id":"2112.06905","title":"GLaM: Efficient Scaling of Language Models with Mixture-of-Experts","created":"2021-12-13","cited_by_count":897,"area":"wm"},{"id":"2106.05974","title":"Scaling Vision with Sparse Mixture of Experts","created":"2021-06-10","cited_by_count":747,"area":"moe"},{"id":"2202.09368","title":"Mixture-of-Experts with Expert Choice Routing","created":"2022-02-18","cited_by_count":623,"area":"moe"},{"id":"2409.02060","title":"OLMoE: Open Mixture-of-Experts Language Models","created":"2024-09-03","cited_by_count":301,"area":"moe"},{"id":"2106.04426","title":"Hash Layers For Large Sparse Models","created":"2021-06-08","cited_by_count":237,"area":null},{"id":"2105.01879","title":"MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space","created":"2021-05-05","cited_by_count":233,"area":null},{"id":"2210.14793","title":"M$^3$ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design","created":"2022-10-26","cited_by_count":98,"area":"moe"},{"id":"2203.14216","title":"Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution","created":"2022-03-27","cited_by_count":96,"area":null},{"id":"2105.09156","title":"Generalizable Person Re-identification with Relevance-aware Mixture of Experts","created":"2021-05-19","cited_by_count":77,"area":"moe"},{"id":"2405.11157","title":"Towards Modular LLMs by Building and Reusing a Library of LoRAs","created":"2024-05-18","cited_by_count":72,"area":null},{"id":"2303.14177","title":"Scaling Expert Language Models with Unsupervised Domain Discovery","created":"2023-03-24","cited_by_count":60,"area":null},{"id":"2303.01610","title":"Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers","created":"2023-03-02","cited_by_count":59,"area":"moe"},{"id":"2310.05707","title":"Guiding Language Model Reasoning with Planning Tokens","created":"2023-10-09","cited_by_count":58,"area":"lr"},{"id":"2405.14297","title":"Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models","created":"2024-05-23","cited_by_count":56,"area":"moe"},{"id":"2210.03885","title":"Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts","created":"2022-10-08","cited_by_count":50,"area":"moe"},{"id":"2105.03036","title":"SpeechMoE: Scaling to Large Acoustic Models with Dynamic Routing Mixture of Experts","created":"2021-05-07","cited_by_count":48,"area":"moe"},{"id":"2204.09636","title":"Residual Mixture of Experts","created":"2022-04-20","cited_by_count":39,"area":"moe"},{"id":"2205.10034","title":"MoESys: A Distributed and Efficient Mixture-of-Experts Training and Inference System for Internet Services","created":"2022-05-20","cited_by_count":37,"area":"moe"},{"id":"2207.10430","title":"The Neural Race Reduction: Dynamics of Abstraction in Gated Networks","created":"2022-07-21","cited_by_count":37,"area":null},{"id":"2306.04073","title":"Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks","created":"2023-06-07","cited_by_count":35,"area":"moe"},{"id":"2306.00008","title":"Brainformers: Trading Simplicity for Efficiency","created":"2023-05-29","cited_by_count":31,"area":null},{"id":"2308.10110","title":"Robust Mixture-of-Expert Training for Convolutional Neural Networks","created":"2023-08-19","cited_by_count":30,"area":"moe"},{"id":"2207.09868","title":"Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing","created":"2022-07-20","cited_by_count":28,"area":"moe"},{"id":"2308.11793","title":"Enhancing NeRF akin to Enhancing LLMs: Generalizable NeRF Transformer with Mixture-of-View-Experts","created":"2023-08-22","cited_by_count":27,"area":"moe"},{"id":"2111.11831","title":"SpeechMoE2: Mixture-of-Experts Model with Improved Routing","created":"2021-11-23","cited_by_count":26,"area":"moe"},{"id":"2401.15969","title":"Routers in Vision Mixture of Experts: An Empirical Study","created":"2024-01-29","cited_by_count":23,"area":"moe"},{"id":"2311.15436","title":"Learning to Skip for Language Modeling","created":"2023-11-26","cited_by_count":19,"area":null},{"id":"2211.11747","title":"NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research","created":"2022-11-15","cited_by_count":18,"area":null},{"id":"2507.00029","title":"LoRA-Mixer: Coordinate Modular LoRA Experts Through Serial Attention Routing","created":"2025-06-17","cited_by_count":18,"area":"moe"},{"id":"2401.10731","title":"Removal then Selection: A Coarse-to-Fine Fusion Perspective for RGB-Infrared Object Detection","created":"2024-01-19","cited_by_count":16,"area":null},{"id":"2204.10598","title":"Sparsely-gated Mixture-of-Expert Layers for CNN Interpretability","created":"2022-04-22","cited_by_count":14,"area":"moe"},{"id":"2211.13491","title":"Spatial Mixture-of-Experts","created":"2022-11-24","cited_by_count":14,"area":"moe"},{"id":"1802.07417","title":"Breaking the gridlock in Mixture-of-Experts: Consistent and Efficient Algorithms","created":"2018-02-21","cited_by_count":13,"area":"moe"},{"id":"2401.02151","title":"Frequency-Adaptive Pan-Sharpening with Mixture of Experts","created":"2024-01-04","cited_by_count":13,"area":null},{"id":"2109.09690","title":"Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes","created":"2021-09-20","cited_by_count":12,"area":null},{"id":"2310.11031","title":"Domain Generalization Using Large Pretrained Models with Mixture-of-Adapters","created":"2023-10-17","cited_by_count":11,"area":null},{"id":"2410.07901","title":"Semi-Supervised Video Desnowing Network via Temporal Decoupling Experts and Distribution-Driven Contrastive Regularization","created":"2024-10-10","cited_by_count":11,"area":null},{"id":"2304.05497","title":"Revisiting Single-gated Mixtures of Experts","created":"2023-04-11","cited_by_count":10,"area":"moe"},{"id":"2403.10616","title":"DiPaCo: Distributed Path Composition","created":"2024-03-15","cited_by_count":10,"area":null},{"id":"2508.05038","title":"HAMoBE: Hierarchical and Adaptive Mixture of Biometric Experts for Video-based Person ReID","created":"2025-08-07","cited_by_count":10,"area":null},{"id":"2402.06512","title":"Multimodal Clinical Trial Outcome Prediction with Large Language Models","created":"2024-02-09","cited_by_count":7,"area":"moe"},{"id":"2402.14891","title":"LLMBind: A Unified Modality-Task Integration Framework","created":"2024-02-22","cited_by_count":7,"area":"mllm"},{"id":"2504.01337","title":"Advancing MoE Efficiency: A Collaboration-Constrained Routing (C2R) Strategy for Better Expert Parallelism Design","created":"2025-04-02","cited_by_count":6,"area":"moe"},{"id":"2204.03178","title":"3M: Multi-loss, Multi-path and Multi-level Neural Networks for speech recognition","created":"2022-04-07","cited_by_count":5,"area":"moe"},{"id":"2410.03529","title":"No Need to Talk: Asynchronous Mixture of Language Models","created":"2024-10-04","cited_by_count":5,"area":null},{"id":"2501.07451","title":"A Survey on Dynamic Neural Networks: from Computer Vision to Multi-modal Sensor Fusion","created":"2025-01-13","cited_by_count":5,"area":null},{"id":"2105.11373","title":"Large-Scale Attribute-Object Compositions","created":"2021-05-24","cited_by_count":4,"area":null},{"id":"2402.01093","title":"Need a Small Specialized Language Model? Plan Early!","created":"2024-02-02","cited_by_count":4,"area":null},{"id":"2106.12475","title":"Mixtures of Deep Neural Experts for Automated Speech Scoring","created":"2021-06-23","cited_by_count":3,"area":null},{"id":"2204.08899","title":"Towards Efficient Single Image Dehazing and Desnowing","created":"2022-04-19","cited_by_count":3,"area":null},{"id":"2212.10066","title":"RepMode: Learning to Re-parameterize Diverse Experts for Subcellular Structure Prediction","created":"2022-12-20","cited_by_count":3,"area":null},{"id":"2304.02229","title":"Mixed Regression via Approximate Message Passing","created":"2023-04-05","cited_by_count":3,"area":"moe"},{"id":"2311.14747","title":"HOPE: A Memory-Based and Composition-Aware Framework for Zero-Shot Learning with Hopfield Network and Soft Mixture of Experts","created":"2023-11-23","cited_by_count":3,"area":"moe"},{"id":"2402.11919","title":"Unraveling Complex Data Diversity in Underwater Acoustic Target Recognition through Convolution-based Mixture of Experts","created":"2024-02-19","cited_by_count":3,"area":"moe"},{"id":"2502.01804","title":"Soup-of-Experts: Pretraining Specialist Models via Parameters Averaging","created":"2025-02-03","cited_by_count":3,"area":null},{"id":"2406.11919","title":"Graph Knowledge Distillation to Mixture of Experts","created":"2024-06-17","cited_by_count":2,"area":"moe"},{"id":"2301.05865","title":"Gated Self-supervised Learning For Improving Supervised Learning","created":"2023-01-14","cited_by_count":1,"area":null},{"id":"2507.11834","title":"CorrMoE: Mixture of Experts with De-stylization Learning for Cross-Scene and Cross-Domain Correspondence Pruning","created":"2025-07-16","cited_by_count":1,"area":"moe"},{"id":"2606.18105","title":"OmniPlan: An Adaptive Framework for Timely and Near-Optimal Network Planning Optimization","created":"2026-06-16","cited_by_count":1,"area":null},{"id":"2606.23739","title":"Systematic Exploration of 4-Expert Heterogeneous Mixture-of-Experts via Automated Pipeline Search","created":"2026-06-21","cited_by_count":1,"area":"moe"},{"id":"2303.04115","title":"Predicted Embedding Power Regression for Large-Scale Out-of-Distribution Detection","created":"2023-03-07","cited_by_count":0,"area":null},{"id":"2504.18241","title":"Switch-Based Multi-Part Neural Network","created":"2025-04-25","cited_by_count":0,"area":null},{"id":"2505.12007","title":"Multi-modal Collaborative Optimization and Expansion Network for Event-assisted Single-eye Expression Recognition","created":"2025-05-17","cited_by_count":0,"area":"moe"},{"id":"2509.20073","title":"SHMoAReg: Spark Deformable Image Registration via Spatial Heterogeneous Mixture of Experts and Attention Heads","created":"2025-09-24","cited_by_count":0,"area":"moe"},{"id":"2510.22909","title":"Rethinking Inference Placement for Deep Learning across Edge and Cloud Platforms: A Multi-Objective Optimization Perspective and Future Directions","created":"2025-10-27","cited_by_count":0,"area":"lr"},{"id":"2510.27182","title":"SERFLOW: A Cross-Service Cost Optimization Framework for SLO-Aware Dynamic ML Inference","created":"2025-10-31","cited_by_count":0,"area":null},{"id":"2603.12607","title":"CarPLAN: Context-Adaptive and Robust Planning with Dynamic Scene Awareness for Autonomous Driving","created":"2026-03-13","cited_by_count":0,"area":"driving"}],"next_cursor":null}