3 citations · 4 across the 4 of their papers we have counts for
4 papers
Joint-stochastic-approximation Random Fields with Application to Semi-supervised Learning
Yunfu Song, Zhijian Ou
Our examination of deep generative models (DGMs) developed for semi-supervised learning (SSL), mainly GANs and VAEs, reveals two problems. First, mode missing and mode covering phe…
An empirical study of domain-agnostic semi-supervised learning via energy-based models: joint-training and pre-training
Yunfu Song, Huahuan Zheng, Zhijian Ou
A class of recent semi-supervised learning (SSL) methods heavily rely on domain-specific data augmentations. In contrast, generative SSL methods involve unsupervised learning based…
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…