3 citations · 3 across the 1 of their papers we have counts for
3 papers
stat.ML2019
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…
stat.ML2019
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…
stat.ML2019★ 3 cited
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…