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Mixture-of-Experts

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Showing 301–325 of 1312 papers

TitleStatusHype
MoHAVE: Mixture of Hierarchical Audio-Visual Experts for Robust Speech Recognition—0
Training Sparse Mixture Of Experts Text Embedding ModelsCode4
Memory Analysis on the Training Course of DeepSeek Models—0
MoENAS: Mixture-of-Expert based Neural Architecture Search for jointly Accurate, Fair, and Robust Edge Deep Neural Networks—0
MoETuner: Optimized Mixture of Expert Serving with Balanced Expert Placement and Token Routing—0
Jakiro: Boosting Speculative Decoding with Decoupled Multi-Head via MoECode1
MoEMba: A Mamba-based Mixture of Experts for High-Density EMG-based Hand Gesture Recognition—0
Klotski: Efficient Mixture-of-Expert Inference via Expert-Aware Multi-Batch PipelineCode0
Mol-MoE: Training Preference-Guided Routers for Molecule GenerationCode0
Leveraging Pre-Trained Models for Multimodal Class-Incremental Learning under Adaptive Fusion—0
Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts—0
fMoE: Fine-Grained Expert Offloading for Large Mixture-of-Experts Serving—0
Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient—0
Mixture of neural operator experts for learning boundary conditions and model selection—0
CMoE: Fast Carving of Mixture-of-Experts for Efficient LLM InferenceCode1
Optimizing Robustness and Accuracy in Mixture of Experts: A Dual-Model Approach—0
ReGNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction—0
M2R2: Mixture of Multi-Rate Residuals for Efficient Transformer Inference—0
Brief analysis of DeepSeek R1 and it's implications for Generative AI—0
CLIP-UP: A Simple and Efficient Mixture-of-Experts CLIP Training Recipe with Sparse Upcycling—0
MJ-VIDEO: Fine-Grained Benchmarking and Rewarding Video Preferences in Video Generation—0
MergeME: Model Merging Techniques for Homogeneous and Heterogeneous MoEs—0
UniGraph2: Learning a Unified Embedding Space to Bind Multimodal GraphsCode1
Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic PerspectiveCode0
Sigmoid Self-Attention has Lower Sample Complexity than Softmax Self-Attention: A Mixture-of-Experts Perspective—0
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