167 citations · 1k across the 81 of their papers we have counts for
8 papers · 2 filters
Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference
Erik Nijkamp, Bo Pang, Tian Han +3
This paper studies the fundamental problem of learning deep generative models that consist of multiple layers of latent variables organized in top-down architectures. Such models h…
Flow Contrastive Estimation of Energy-Based Models
Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma +3
This paper studies a training method to jointly estimate an energy-based model and a flow-based model, in which the two models are iteratively updated based on a shared adversarial…
Representation Learning: A Statistical Perspective
Jianwen Xie, Ruiqi Gao, Erik Nijkamp +2
Learning representations of data is an important problem in statistics and machine learning. While the origin of learning representations can be traced back to factor analysis and…
Deep Unsupervised Clustering with Clustered Generator Model
Dandan Zhu, Tian Han, Linqi Zhou +2
This paper addresses the problem of unsupervised clustering which remains one of the most fundamental challenges in machine learning and artificial intelligence. We propose the clu…
Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model
Erik Nijkamp, Mitch Hill, Song-Chun Zhu +1
This paper studies a curious phenomenon in learning energy-based model (EBM) using MCMC. In each learning iteration, we generate synthesized examples by running a non-convergent, n…
On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models
Erik Nijkamp, Mitch Hill, Tian Han +2
This study investigates the effects of Markov chain Monte Carlo (MCMC) sampling in unsupervised Maximum Likelihood (ML) learning. Our attention is restricted to the family of unnor…