5 citations · 18 across the 11 of their papers we have counts for
9 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…
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…