Classifier Weighted Mixture models
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Abstract (arXiv)
This paper proposes an extension of standard mixture stochastic models, by replacing the constant mixture weights with functional weights defined using a classifier. Classifier Weighted Mixtures enable straightforward density evaluation, explicit sampling, and enhanced expressivity in variational estimation problems, without increasing the number of components nor the complexity of the mixture components.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 28 references that are in the map. First posted on arXiv 6 Jan 2025.
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9 of 28 references.
References · 9
- 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,739
- 2015Variational Inference with Normalizing Flowscited by 2,170
- 2016Density estimation using Real NVPcited by 2,042
- 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,323
- 2016Improving Variational Inference with Inverse Autoregressive Flowcited by 640
- 2018Implicit Reparameterization Gradientscited by 85
- 2018Variance reduction properties of the reparameterization trickcited by 41
- 2021Go with the Flows: Mixtures of Normalizing Flows for Point Cloud Generation and Reconstructioncited by 16
- 2017Lost Relatives of the Gumbel Trickcited by 6
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