243 citations · 461 across the 25 of their papers we have counts for
7 papers · 1 filter
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…
A Generative Model for Sampling High-Performance and Diverse Weights for Neural Networks
Lior Deutsch, Erik Nijkamp, Yu Yang
Recent work on mode connectivity in the loss landscape of deep neural networks has demonstrated that the locus of (sub-)optimal weight vectors lies on continuous paths. In this wor…
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…
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…