{"total":85,"rows":[{"id":"2503.07137","title":"A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applications","created":"2025-03-10","cited_by_count":91,"area":"moe"},{"id":"2402.03226","title":"FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion","created":"2024-02-05","cited_by_count":68,"area":"moe"},{"id":"2304.02806","title":"Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling","created":"2023-04-06","cited_by_count":64,"area":"moe"},{"id":"2310.15166","title":"Large Language Models are Visual Reasoning Coordinators","created":"2023-10-23","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":"2310.18859","title":"SiDA-MoE: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models","created":"2023-10-29","cited_by_count":38,"area":"moe"},{"id":"2407.09590","title":"Diversifying the Expert Knowledge for Task-Agnostic Pruning in Sparse Mixture-of-Experts","created":"2024-07-12","cited_by_count":37,"area":"moe"},{"id":"2406.03751","title":"Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting","created":"2024-06-06","cited_by_count":34,"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":"2309.13850","title":"Statistical Perspective of Top-K Sparse Softmax Gating Mixture of Experts","created":"2023-09-25","cited_by_count":29,"area":"moe"},{"id":"2404.08452","title":"MoE-FFD: Mixture of Experts for Generalized and Parameter-Efficient Face Forgery Detection","created":"2024-04-12","cited_by_count":27,"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":"2402.02952","title":"On Least Square Estimation in Softmax Gating Mixture of Experts","created":"2024-02-05","cited_by_count":23,"area":"moe"},{"id":"2412.20665","title":"SM3Det: A Unified Model for Multi-Modal Remote Sensing Object Detection","created":"2024-12-30","cited_by_count":22,"area":null},{"id":"2404.07794","title":"DGMamba: Domain Generalization via Generalized State Space Model","created":"2024-04-11","cited_by_count":21,"area":"ssm"},{"id":"2408.06793","title":"Layerwise Recurrent Router for Mixture-of-Experts","created":"2024-08-13","cited_by_count":21,"area":"moe"},{"id":"2306.06209","title":"Backdoor Attack with Sparse and Invisible Trigger","created":"2023-05-11","cited_by_count":20,"area":null},{"id":"2312.04693","title":"GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts","created":"2023-12-07","cited_by_count":19,"area":"moe"},{"id":"2406.11256","title":"Dynamic Data Mixing Maximizes Instruction Tuning for Mixture-of-Experts","created":"2024-06-17","cited_by_count":19,"area":"moe"},{"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":"2310.10908","title":"Unlocking Emergent Modularity in Large Language Models","created":"2023-10-17","cited_by_count":17,"area":null},{"id":"2309.16483","title":"Rethinking Domain Generalization: Discriminability and Generalizability","created":"2023-09-28","cited_by_count":16,"area":null},{"id":"2405.14131","title":"Statistical Advantages of Perturbing Cosine Router in Mixture of Experts","created":"2024-05-23","cited_by_count":16,"area":"moe"},{"id":"2409.06669","title":"DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models","created":"2024-09-10","cited_by_count":15,"area":"moe"},{"id":"2407.15085","title":"Learn to Preserve and Diversify: Parameter-Efficient Group with Orthogonal Regularization for Domain Generalization","created":"2024-07-21","cited_by_count":14,"area":null},{"id":"2303.07527","title":"Domain Generalization via Nuclear Norm Regularization","created":"2023-03-13","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":"2309.16460","title":"Diverse Target and Contribution Scheduling for Domain Generalization","created":"2023-09-28","cited_by_count":10,"area":null},{"id":"2403.07965","title":"Conditional computation in neural networks: principles and research trends","created":"2024-03-12","cited_by_count":10,"area":"moe"},{"id":"2503.01359","title":"DeRS: Towards Extremely Efficient Upcycled Mixture-of-Experts Models","created":"2025-03-03","cited_by_count":10,"area":"moe"},{"id":"2410.14574","title":"MomentumSMoE: Integrating Momentum into Sparse Mixture of Experts","created":"2024-10-18","cited_by_count":9,"area":"moe"},{"id":"2410.19225","title":"Hierarchical Mixture of Experts: Generalizable Learning for High-Level Synthesis","created":"2024-10-25","cited_by_count":9,"area":"moe"},{"id":"2401.09716","title":"HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization","created":"2024-01-18","cited_by_count":8,"area":null},{"id":"2309.03004","title":"A Theoretical Explanation of Activation Sparsity through Flat Minima and Adversarial Robustness","created":"2023-09-06","cited_by_count":7,"area":null},{"id":"2504.08020","title":"Learning Fine-grained Domain Generalization via Hyperbolic State Space Hallucination","created":"2025-04-10","cited_by_count":7,"area":null},{"id":"2507.01351","title":"Long-Tailed Distribution-Aware Router For Mixture-of-Experts in Large Vision-Language Model","created":"2025-07-02","cited_by_count":7,"area":"mllm"},{"id":"2307.08187","title":"An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration","created":"2023-07-17","cited_by_count":5,"area":null},{"id":"2310.10008","title":"Towards Unified and Effective Domain Generalization","created":"2023-10-16","cited_by_count":5,"area":null},{"id":"2510.10584","title":"Equipping Vision Foundation Model with Mixture of Experts for Out-of-Distribution Detection","created":"2025-10-12","cited_by_count":5,"area":"moe"},{"id":"2410.16020","title":"START: A Generalized State Space Model with Saliency-Driven Token-Aware Transformation","created":"2024-10-21","cited_by_count":4,"area":"ssm"},{"id":"2503.03213","title":"Convergence Rates for Softmax Gating Mixture of Experts","created":"2025-03-05","cited_by_count":4,"area":"moe"},{"id":"2504.09990","title":"Correlative and Discriminative Label Grouping for Multi-Label Visual Prompt Tuning","created":"2025-04-14","cited_by_count":4,"area":"moe"},{"id":"2505.10579","title":"Bias and Generalizability of Foundation Models across Datasets in Breast Mammography","created":"2025-05-14","cited_by_count":4,"area":null},{"id":"2512.19765","title":"How Many Experts Are Enough? Towards Optimal Semantic Specialization for Mixture-of-Experts","created":"2025-12-21","cited_by_count":4,"area":"moe"},{"id":"2404.19394","title":"CLIP-Mamba: CLIP Pretrained Mamba Models with OOD and Hessian Evaluation","created":"2024-04-30","cited_by_count":3,"area":"ssm"},{"id":"2504.06572","title":"Domain Generalization via Discrete Codebook Learning","created":"2025-04-09","cited_by_count":3,"area":null},{"id":"2505.14088","title":"Generalizable Multispectral Land Cover Classification via Frequency-Aware Mixture of Low-Rank Token Experts","created":"2025-05-20","cited_by_count":3,"area":"moe"},{"id":"2511.02331","title":"RoME: Domain-Robust Mixture-of-Experts for MILP Solution Prediction across Domains","created":"2025-11-04","cited_by_count":3,"area":"moe"},{"id":"2311.00285","title":"Mixture-of-Experts for Open Set Domain Adaptation: A Dual-Space Detection Approach","created":"2023-11-01","cited_by_count":2,"area":"moe"},{"id":"2406.11919","title":"Graph Knowledge Distillation to Mixture of Experts","created":"2024-06-17","cited_by_count":2,"area":"moe"},{"id":"2410.08003","title":"More Experts Than Galaxies: Conditionally-overlapping Experts With Biologically-Inspired Fixed Routing","created":"2024-10-10","cited_by_count":2,"area":"moe"},{"id":"2411.07834","title":"Towards Vision Mixture of Experts for Wildlife Monitoring on the Edge","created":"2024-11-12","cited_by_count":2,"area":"moe"},{"id":"2502.10716","title":"Why Domain Generalization Fail? A View of Necessity and Sufficiency","created":"2025-02-15","cited_by_count":2,"area":null},{"id":"2509.24332","title":"Towards Generalizable PDE Dynamics Forecasting via Physics-Guided Invariant Learning","created":"2025-09-29","cited_by_count":2,"area":null},{"id":"2510.15683","title":"Mixture of Experts Approaches in Dense Retrieval Tasks","created":"2025-10-17","cited_by_count":2,"area":"moe"},{"id":"2601.08790","title":"Aggregating Diverse Cue Experts for AI-Generated Image Detection","created":"2026-01-13","cited_by_count":2,"area":null},{"id":"2310.03007","title":"Towards Domain-Specific Features Disentanglement for Domain Generalization","created":"2023-10-04","cited_by_count":1,"area":null},{"id":"2312.16243","title":"Mixture Data for Training Cannot Ensure Out-of-distribution Generalization","created":"2023-12-25","cited_by_count":1,"area":null},{"id":"2406.03125","title":"Space Decomposition for Sentence Embedding","created":"2024-06-05","cited_by_count":1,"area":null},{"id":"2410.00373","title":"Robust Traffic Forecasting against Spatial Shift over Years","created":"2024-10-01","cited_by_count":1,"area":null},{"id":"2504.08019","title":"DGFamba: Learning Flow Factorized State Space for Visual Domain Generalization","created":"2025-04-10","cited_by_count":1,"area":"ssm"},{"id":"2505.18455","title":"On Minimax Estimation of Parameters in Softmax-Contaminated Mixture of Experts","created":"2025-05-24","cited_by_count":1,"area":"moe"},{"id":"2506.20575","title":"Exploring Graph-Transformer Out-of-Distribution Generalization Abilities","created":"2025-06-25","cited_by_count":1,"area":null},{"id":"2511.10107","title":"RobIA: Robust Instance-aware Continual Test-time Adaptation for Deep Stereo","created":"2025-11-13","cited_by_count":1,"area":null},{"id":"2601.14792","title":"Robustness of Mixtures of Experts to Feature Noise","created":"2026-01-21","cited_by_count":1,"area":"moe"},{"id":"2603.13352","title":"Local Precise Refinement: A Dual-Gated Mixture-of-Experts for Enhancing Foundation Model Generalization against Spectral Shifts","created":"2026-03-08","cited_by_count":1,"area":"moe"},{"id":"2605.05136","title":"CPCANet: Deep Unfolding Common Principal Component Analysis for Domain Generalization","created":"2026-05-06","cited_by_count":1,"area":null},{"id":"2607.16726","title":"Can Experts Adapt Without Training? On Test-Time Modality Generalization in MVLMs","created":"2026-07-18","cited_by_count":1,"area":"mllm"},{"id":"2503.18998","title":"FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognition","created":"2025-03-24","cited_by_count":0,"area":null},{"id":"2504.02272","title":"Generative Classifier for Domain Generalization","created":"2025-04-03","cited_by_count":0,"area":null},{"id":"2508.17595","title":"TinyGiantVLM: A Lightweight Vision-Language Architecture for Spatial Reasoning under Resource Constraints","created":"2025-08-25","cited_by_count":0,"area":"lr"},{"id":"2511.01192","title":"DEER: Disentangled Mixture of Experts with Instance-Adaptive Routing for Generalizable Machine-Generated Text Detection","created":"2025-11-03","cited_by_count":0,"area":"moe"},{"id":"2511.11232","title":"DoReMi: Bridging 3D Domains via Topology-Aware Domain-Representation Mixture of Experts","created":"2025-11-14","cited_by_count":0,"area":"moe"},{"id":"2511.18314","title":"AnyExperts: On-Demand Expert Allocation for Multimodal Language Models with Mixture of Expert","created":"2025-11-23","cited_by_count":0,"area":"mllm"},{"id":"2602.00939","title":"Improving Minimax Estimation Rates for Contaminated Mixture of Multinomial Logistic Experts via Expert Heterogeneity","created":"2026-01-31","cited_by_count":0,"area":"moe"},{"id":"2602.01466","title":"Rethinking Multinomial Logistic Mixture of Experts with Sigmoid Gating Function","created":"2026-02-01","cited_by_count":0,"area":"lr"},{"id":"2603.27965","title":"ExFusion: Efficient Transformer Training via Multi-Experts Fusion","created":"2026-03-30","cited_by_count":0,"area":"moe"},{"id":"2604.16892","title":"CrossFlowDG: Bridging the Modality Gap with Cross-modal Flow Matching for Domain Generalization","created":"2026-04-18","cited_by_count":0,"area":"diffusion"},{"id":"2604.18842","title":"Multi-Domain Learning with Global Expert Mapping","created":"2026-04-20","cited_by_count":0,"area":"moe"},{"id":"2606.04888","title":"HD-DinoMoE: A Class-Aware Hierarchical Dual Mixture-of-Experts Network for Scleral Anomaly Segmentation in Complex Acquisition Scenarios","created":"2026-06-03","cited_by_count":0,"area":null},{"id":"2606.08814","title":"STAR: Rethinking MoE Routing as Structure-Aware Subspace Learning","created":"2026-06-07","cited_by_count":0,"area":"moe"},{"id":"2606.25665","title":"Learning Subset-Shared Invariances for Domain Generalization with Mixture-of-Experts","created":"2026-06-24","cited_by_count":0,"area":"moe"},{"id":"2607.22117","title":"Projection Pursuit CPCANet for Domain Generalization","created":"2026-07-24","cited_by_count":0,"area":null},{"id":"2608.10392","title":"Share First, Route What Remains: A Unified Framework for Token-Adaptive MoE Computation","created":"2026-08-11","cited_by_count":0,"area":"moe"},{"id":"2608.11699","title":"STAR: A Spatial-Topology Aware Routing Framework for Generalizable 3D Scene Understanding","created":"2026-08-12","cited_by_count":0,"area":"moe"}],"next_cursor":null}