29 citations · 44 across the 2 of their papers we have counts for
2 papers
stat.ML2017★ 15 cited
Learning Deep Energy Models: Contrastive Divergence vs. Amortized MLE
Qiang Liu, Dilin Wang
We propose a number of new algorithms for learning deep energy models and demonstrate their properties. We show that our SteinCD performs well in term of test likelihood, while Ste…
stat.ML2017★ 29 cited
Learning to Draw Samples with Amortized Stein Variational Gradient Descent
Yihao Feng, Dilin Wang, Qiang Liu
We propose a simple algorithm to train stochastic neural networks to draw samples from given target distributions for probabilistic inference. Our method is based on iteratively ad…