Product of Experts

Also known as PoE

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Latest papers

9 papers0 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

CLEAR: Class-wise Expert Aggregation with Structured Sampling for Long-Tailed Classification
Aug 11, 2026Gawon LimLong-Tailed Classification Ensemble Learning
School of Information Sciences University of Illinois Urbana-Champaign
Auditing Instruction-Trajectory Mismatches in Multimodal Robot Demonstrations
Aug 8, 2026Simon Holk, Ryosuke Takanami, Tatsuya Matsushima +4Product of Experts Robot Imitation Learning
AI Robot Association (AIRoA), Tokyo, Japan
Model Merging as Probabilistic Inference in Fine-Tuning Parameter Space
Jul 2, 2026Long Minh Bui, Tuan Anh Le Van, Tung Phi Duc +3Product of Experts Energy-Based Models
Washington State University · Hanoi University of Science and Technology
Diffusion Language Model Parallel Decoding via Product-of-Experts Bridge
Jun 6, 2026Juntong Shi, Brian L. Trippe, Jure Leskovec +2Importance Sampling Product of Experts
Stanford University
PRA-PoE: Robust Multimodal Alzheimer's Diagnosis with Arbitrary Missing Modalities
May 13, 2026Guangqian Yang, Ye Du, Wenlong Hou +2Missing-Modality Learning Alzheimer's Disease
Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China · Department of Technology Management for Innovation, The University of Tokyo,2026 Japan · Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong SAR, China
Multi-Perspective Transformers in ARC-AGI-2 Challenge
May 1, 2026Caleb Talley, Vedant Tibrewal, Seun Adekunle +3Visual Reasoning Product of Experts
Context as Prior: Bayesian-Inspired Intent Inference for Non-Speaking Agents with a Household Cat Testbed
Apr 30, 2026Wenqian Zhang, Zehao WangBayesian Inference Product of Experts
University of California, Riverside, Riverside, CA
Addressing Ambiguity in Imitation Learning through Product of Experts based Negative Feedback
Mar 27, 2026John Bateman, Andy M. Tyrrell, Jihong ZhuProduct of Experts Learning from Suboptimal Demonstrations
University of York
Parallel In-context Learning for Large Vision Language Models
Mar 17, 2026Shin'ya Yamaguchi, Daiki Chijiwa, Tamao Sakao +1Product of Experts In-Context Learning
NTT

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CLEAR: Class-wise Expert Aggregation with Structured Sampling for Long-Tailed Classification

Gawon Lim

School of Information Sciences University of Illinois Urbana-Champaign

Long-Tailed Classification Ensemble Learning Product of Experts Classification Mixture-of-Experts Models

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Long-tailed classification poses a reliability challenge because models trained on imbalanced data are unevenly reliable across frequent and underrepresented classes. While existing methods address imbalance through re-balancing, adjustment, representation learning, or multi-expert modeling, they rarely estimate which expert should be trusted for each class. This paper proposes CLEAR (Class-wise reLiability-aware Expert Aggregation for long-tailed Recognition), a modular ensemble framework for long-tailed classification. CLEAR generates diverse experts through threshold-based structured sampling while preserving the full label space, then estimates a class-wise trust score for each expert using a smoothed class-wise precision formulation. During inference, expert predictions are combined through class-wise generalized product-of-experts aggregation, allowing different experts to be emphasized for different classes. Experiments on CIFAR-100-LT, ImageNet-LT, and Places-LT across multiple backbones show that CLEAR achieves competitive overall accuracy and particularly strong few-shot performance. These results support class-wise expert reliability as a useful design principle for long-tailed ensemble learning.