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Mixture-of-Experts

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Showing 1051–1075 of 1312 papers

TitleStatusHype
FaVChat: Unlocking Fine-Grained Facail Video Understanding with Multimodal Large Language Models—0
FEAMOE: Fair, Explainable and Adaptive Mixture of Experts—0
Federated learning using mixture of experts—0
Federated Mixture of Experts—0
FedMerge: Federated Personalization via Model Merging—0
FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation—0
FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts—0
Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settings—0
Filtered not Mixed: Stochastic Filtering-Based Online Gating for Mixture of Large Language Models—0
Finding Fantastic Experts in MoEs: A Unified Study for Expert Dropping Strategies and Observations—0
FineQuant: Unlocking Efficiency with Fine-Grained Weight-Only Quantization for LLMs—0
FinTeamExperts: Role Specialized MOEs For Financial Analysis—0
Fixing MoE Over-Fitting on Low-Resource Languages in Multilingual Machine Translation—0
Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models—0
FlexMoE: Scaling Large-scale Sparse Pre-trained Model Training via Dynamic Device Placement—0
FloE: On-the-Fly MoE Inference on Memory-constrained GPU—0
fMoE: Fine-Grained Expert Offloading for Large Mixture-of-Experts Serving—0
FMT:A Multimodal Pneumonia Detection Model Based on Stacking MOE Framework—0
ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation—0
Free Agent in Agent-Based Mixture-of-Experts Generative AI Framework—0
FreqMoE: Dynamic Frequency Enhancement for Neural PDE Solvers—0
Fresh-CL: Feature Realignment through Experts on Hypersphere in Continual Learning—0
From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscape—0
FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models—0
Full-Precision Free Binary Graph Neural Networks—0
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