Generative Models for Learning from Crowds

원문 초록

In this paper, we propose generative probabilistic models for label aggregation. We use Gibbs sampling and a novel variational inference algorithm to perform the posterior inference. Empirical results show that our methods consistently outperform state-of-the-art methods.

함께 살펴볼 논문

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arXiv:2507.11136공통 태그 Variational Inference공식 코드 · Papers with Code 등록 2025-07-15

Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection

arXiv:2506.21408공통 태그 Variational Inference공식 코드 · Papers with Code 등록 2025-06-26

Scalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace Inference

arXiv:2506.13613공통 태그 Variational Inference공식 코드 · Papers with Code 등록 2025-06-16

Variational Inference with Mixtures of Isotropic Gaussians

arXiv:2506.04749공통 태그 Variational Inference공식 코드 · Papers with Code 등록 2025-06-05

Amortized variational transdimensional inference

arXiv:2505.22438공통 태그 Variational Inference공식 코드 · Papers with Code 등록 2025-05-28

Synonymous Variational Inference for Perceptual Image Compression

arXiv:2505.18591공통 태그 Variational Inference공식 코드 · Papers with Code 등록 2025-05-24

Bayesian Meta-Reinforcement Learning with Laplace Variational Recurrent Networks

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