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Found 4 papers, 2 papers with code
An empirical study of domain-agnostic semi-supervised learning via energy-based models: joint-training and pre-training
In contrast, generative SSL methods involve unsupervised learning based on generative models by either joint-training or pre-training, and are more appealing from the perspective of being domain-agnostic, since they do not inherently require data augmentations.
Joint Stochastic Approximation and Its Application to Learning Discrete Latent Variable Models
Although with progress in introducing auxiliary amortized inference models, learning discrete latent variable models is still challenging.
Learning Neural Random Fields with Inclusive Auxiliary Generators
Neural random fields (NRFs), which are defined by using neural networks to implement potential functions in undirected models, provide an interesting family of model spaces for machine learning.
Generative Modeling by Inclusive Neural Random Fields with Applications in Image Generation and Anomaly Detection
With these contributions and results, this paper significantly advances the learning and applications of NRFs to a new level, both theoretically and empirically, which have never been obtained before.
