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Mixture-of-Experts

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Showing 801–825 of 1312 papers

TitleStatusHype
A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning—0
HoME: Hierarchy of Multi-Gate Experts for Multi-Task Learning at Kuaishou—0
LaDiMo: Layer-wise Distillation Inspired MoEfier—0
Understanding the Performance and Estimating the Cost of LLM Fine-TuningCode0
MoC-System: Efficient Fault Tolerance for Sparse Mixture-of-Experts Model Training—0
Mixture-of-Noises Enhanced Forgery-Aware Predictor for Multi-Face Manipulation Detection and Localization—0
HMDN: Hierarchical Multi-Distribution Network for Click-Through Rate Prediction—0
Multimodal Fusion and Coherence Modeling for Video Topic Segmentation—0
MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts—0
PMoE: Progressive Mixture of Experts with Asymmetric Transformer for Continual Learning—0
Distribution Learning for Molecular Regression—0
Time series forecasting with high stakes: A field study of the air cargo industry—0
Mixture of Nested Experts: Adaptive Processing of Visual TokensCode0
Mixture of Modular Experts: Distilling Knowledge from a Multilingual Teacher into Specialized Modular Language ModelsCode0
MOoSE: Multi-Orientation Sharing Experts for Open-set Scene Text RecognitionCode0
Wolf: Captioning Everything with a World Summarization Framework—0
How Lightweight Can A Vision Transformer Be—0
Exploring Domain Robust Lightweight Reward Models based on Router Mechanism—0
Wonderful Matrices: More Efficient and Effective Architecture for Language Modeling Tasks—0
EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classification—0
EVLM: An Efficient Vision-Language Model for Visual Understanding—0
Mixture of Experts with Mixture of Precisions for Tuning Quality of Service—0
Mixture of Experts based Multi-task Supervise Learning from Crowds—0
Discussion: Effective and Interpretable Outcome Prediction by Training Sparse Mixtures of Linear Experts—0
MoE-DiffIR: Task-customized Diffusion Priors for Universal Compressed Image Restoration—0
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