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Mixture-of-Experts

Papers

Showing 676–700 of 1312 papers

TitleStatusHype
D^2MoE: Dual Routing and Dynamic Scheduling for Efficient On-Device MoE-based LLM Serving—0
DADNN: Multi-Scene CTR Prediction via Domain-Aware Deep Neural Network—0
DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models—0
Data Expansion using Back Translation and Paraphrasing for Hate Speech Detection—0
Decoding Knowledge Attribution in Mixture-of-Experts: A Framework of Basic-Refinement Collaboration and Efficiency Analysis—0
Deep Gaussian Covariance Network—0
Deep Learning Mixture-of-Experts Approach for Cytotoxic Edema Assessment in Infants and Children—0
Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models—0
Demystifying Softmax Gating Function in Gaussian Mixture of Experts—0
Denoising OCT Images Using Steered Mixture of Experts with Multi-Model Inference—0
Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models—0
DeRS: Towards Extremely Efficient Upcycled Mixture-of-Experts Models—0
Detecting Financial Fraud with Hybrid Deep Learning: A Mix-of-Experts Approach to Sequential and Anomalous Patterns—0
dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis—0
Differentially Private Training of Mixture of Experts Models—0
Direct Neural Machine Translation with Task-level Mixture of Experts models—0
Discussion: Effective and Interpretable Outcome Prediction by Training Sparse Mixtures of Linear Experts—0
Disguise without Disruption: Utility-Preserving Face De-Identification—0
Distribution Learning for Molecular Regression—0
Diverse Machine Translation with a Single Multinomial Latent Variable—0
Diversified Dynamic Routing for Vision Tasks—0
Diversifying the Expert Knowledge for Task-Agnostic Pruning in Sparse Mixture-of-Experts—0
Diversifying the Mixture-of-Experts Representation for Language Models with Orthogonal Optimizer—0
Diversity-Promoting Bayesian Learning of Latent Variable Models—0
Divide-and-Conquer: Cold-Start Bundle Recommendation via Mixture of Diffusion Experts—0
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