24 citations · 114 across the 41 of their papers we have counts for
4 papers · 1 filter
Persistently Trained, Diffusion-assisted Energy-based Models
Xinwei Zhang, Zhiqiang Tan, Zhijian Ou
Maximum likelihood (ML) learning for energy-based models (EBMs) is challenging, partly due to non-convergence of Markov chain Monte Carlo.Several variations of ML learning have bee…
Joint Stochastic Approximation and Its Application to Learning Discrete Latent Variable Models
Zhijian Ou, Yunfu Song
Although with progress in introducing auxiliary amortized inference models, learning discrete latent variable models is still challenging. In this paper, we show that the annoying…
Generative Modeling by Inclusive Neural Random Fields with Applications in Image Generation and Anomaly Detection
Yunfu Song, Zhijian Ou
Neural random fields (NRFs), referring to a class of generative models that use neural networks to implement potential functions in random fields (a.k.a. energy-based models), are…
Learning Sparse Structured Ensembles with SG-MCMC and Network Pruning
Yichi Zhang, Zhijian Ou
An ensemble of neural networks is known to be more robust and accurate than an individual network, however usually with linearly-increased cost in both training and testing. In thi…