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Mixture-of-Experts

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Showing 401–425 of 1312 papers

TitleStatusHype
Object Detection using Event Camera: A MoE Heat Conduction based Detector and A New Benchmark DatasetCode2
UniPaint: Unified Space-time Video Inpainting via Mixture-of-Experts—0
An Entailment Tree Generation Approach for Multimodal Multi-Hop Question Answering with Mixture-of-Experts and Iterative Feedback Mechanism—0
SAME: Learning Generic Language-Guided Visual Navigation with State-Adaptive Mixture of ExpertsCode1
RSUniVLM: A Unified Vision Language Model for Remote Sensing via Granularity-oriented Mixture of ExpertsCode1
Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers—0
Steps are all you need: Rethinking STEM Education with Prompt Engineering—0
Monet: Mixture of Monosemantic Experts for TransformersCode2
Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond—0
Yi-Lightning Technical Report—0
MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption—0
Mixture of Experts for Node Classification—0
HiMoE: Heterogeneity-Informed Mixture-of-Experts for Fair Spatial-Temporal Forecasting—0
LaVIDE: A Language-Vision Discriminator for Detecting Changes in Satellite Image with Map References—0
On the effectiveness of discrete representations in sparse mixture of experts—0
Mixture of Cache-Conditional Experts for Efficient Mobile Device Inference—0
Complexity Experts are Task-Discriminative Learners for Any Image Restoration—0
Mixture of Experts in Image Classification: What's the Sweet Spot?—0
UOE: Unlearning One Expert Is Enough For Mixture-of-experts LLMS—0
Condense, Don't Just Prune: Enhancing Efficiency and Performance in MoE Layer PruningCode1
Enhancing Code-Switching ASR Leveraging Non-Peaky CTC Loss and Deep Language Posterior Injection—0
H^3Fusion: Helpful, Harmless, Honest Fusion of Aligned LLMsCode0
LDACP: Long-Delayed Ad Conversions Prediction Model for Bidding Strategy—0
MH-MoE: Multi-Head Mixture-of-Experts—0
LLaMA-MoE v2: Exploring Sparsity of LLaMA from Perspective of Mixture-of-Experts with Post-TrainingCode2
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