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20242026
most citedModel Selection for Gaussian-gated Gaussian Mixture of Experts Using Dendrograms of Mixing Measures

1 citations · 1 across the 5 of their papers we have counts for

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5 papers

stat.ML2026

Fast Model Selection and Stable Optimization for Softmax-Gated Multinomial-Logistic Mixture of Experts Models

TrungKhang Tran, TrungTin Nguyen, Md Abul Bashar +3

Mixture-of-Experts (MoE) architectures combine specialized predictors through a learned gate and are effective across regression and classification, but for classification with sof…

eess.IV2025

SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation

Gia Huy Thai, Hoang-Nguyen Vu, Anh-Minh Phan +4

The significant variability in cell size and shape continues to pose a major obstacle in computer-assisted cancer detection on gigapixel Whole Slide Images (WSIs), due to cellular…

stat.ML2025

Dendrograms of Mixing Measures for Softmax-Gated Gaussian Mixture of Experts: Consistency Without Model Sweeps

Do Tien Hai, Trung Nguyen Mai, TrungTin Nguyen +3

We develop a unified statistical framework for softmax-gated Gaussian mixture of experts (SGMoE) that addresses three long-standing obstacles in parameter estimation and model sele…

stat.ML2025★ 1 cited

Model Selection for Gaussian-gated Gaussian Mixture of Experts Using Dendrograms of Mixing Measures

Tuan Thai, TrungTin Nguyen, Dat Do +2

Mixture of Experts (MoE) models constitute a widely utilized class of ensemble learning approaches in statistics and machine learning, known for their flexibility and computational…

cs.CV2024

X-Drive: Cross-modality consistent multi-sensor data synthesis for driving scenarios

Yichen Xie, Chenfeng Xu, Chensheng Peng +6

Recent advancements have exploited diffusion models for the synthesis of either LiDAR point clouds or camera image data in driving scenarios. Despite their success in modeling sing…