mlsanityData to 14 Oct 2026

Fully Variational Noise-Contrastive Estimation

arXiv 2304.02473 ↗First posted 4 Apr 2023cs.LG cs.CVcited by 1

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Abstract (arXiv)

By using the underlying theory of proper scoring rules, we design a family of noise-contrastive estimation (NCE) methods that are tractable for latent variable models. Both terms in the underlying NCE loss, the one using data samples and the one using noise samples, can be lower-bounded as in variational Bayes, therefore we call this family of losses fully variational noise-contrastive estimation. Variational autoencoders are a particular example in this family and therefore can be also understood as separating real data from synthetic samples using an appropriate classification loss. We further discuss other instances in this family of fully variational NCE objectives and indicate differences in their empirical behavior.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 32 references that are in the map. First posted on arXiv 4 Apr 2023.

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17 of 32 references.

References · 17

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5 more
  1. 2020Why Normalizing Flows Fail to Detect Out-of-Distribution Datacited by 221
  2. 2019From Variational to Deterministic Autoencoderscited by 133
  3. 2018Adversarially Learned Anomaly Detectioncited by 115
  4. 2018Conditional Noise-Contrastive Estimation of Unnormalised Modelscited by 25
  5. 2018Variational Noise-Contrastive Estimationcited by 6

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Citing papers per year: 2024: 1.

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  1. 2024Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectivescited by 30