48 citations · 144 across the 28 of their papers we have counts for
6 papers · 1 filter
Learning Latent Space Energy-Based Prior Model
Bo Pang, Tian Han, Erik Nijkamp +2
We propose to learn energy-based model (EBM) in the latent space of a generator model, so that the EBM serves as a prior model that stands on the top-down network of the generator…
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…
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…
Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inference Model
Tian Han, Erik Nijkamp, Xiaolin Fang +3
This paper proposes the divergence triangle as a framework for joint training of generator model, energy-based model and inference model. The divergence triangle is a compact and s…
A Tale of Three Probabilistic Families: Discriminative, Descriptive and Generative Models
Ying Nian Wu, Ruiqi Gao, Tian Han +1
The pattern theory of Grenander is a mathematical framework where patterns are represented by probability models on random variables of algebraic structures. In this paper, we revi…
Alternating Back-Propagation for Generator Network
Tian Han, Yang Lu, Song-Chun Zhu +1
This paper proposes an alternating back-propagation algorithm for learning the generator network model. The model is a non-linear generalization of factor analysis. In this model,…