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Mixture-of-Experts

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Showing 951–975 of 1312 papers

TitleStatusHype
Towards Convergence Rates for Parameter Estimation in Gaussian-gated Mixture of Experts—0
Locking and Quacking: Stacking Bayesian model predictions by log-pooling and superposition—0
Alternating Gradient Descent and Mixture-of-Experts for Integrated Multimodal Perception—0
Steered Mixture-of-Experts Autoencoder Design for Real-Time Image Modelling and Denoising—0
Demystifying Softmax Gating Function in Gaussian Mixture of Experts—0
Towards Being Parameter-Efficient: A Stratified Sparsely Activated Transformer with Dynamic CapacityCode0
Unicorn: A Unified Multi-tasking Model for Supporting Matching Tasks in Data IntegrationCode1
Pipeline MoE: A Flexible MoE Implementation with Pipeline Parallelism—0
Revisiting Single-gated Mixtures of Experts—0
FlexMoE: Scaling Large-scale Sparse Pre-trained Model Training via Dynamic Device Placement—0
Mixed Regression via Approximate Message Passing—0
Long-Tailed Visual Recognition via Self-Heterogeneous Integration with Knowledge ExcavationCode1
Re-IQA: Unsupervised Learning for Image Quality Assessment in the WildCode1
Steered Mixture of Experts Regression for Image Denoising with Multi-Model-Inference—0
Information Maximizing Curriculum: A Curriculum-Based Approach for Imitating Diverse SkillsCode0
WM-MoE: Weather-aware Multi-scale Mixture-of-Experts for Blind Adverse Weather Removal—0
Disguise without Disruption: Utility-Preserving Face De-Identification—0
Improving Transformer Performance for French Clinical Notes Classification Using Mixture of Experts on a Limited Dataset—0
Learning A Sparse Transformer Network for Effective Image DerainingCode2
HDformer: A Higher Dimensional Transformer for Diabetes Detection Utilizing Long Range Vascular Signals—0
MCR-DL: Mix-and-Match Communication Runtime for Deep Learning—0
Scaling Vision-Language Models with Sparse Mixture of Experts—0
A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training—0
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference—0
Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable TransformersCode1
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