306 citations · 682 across the 25 of their papers we have counts for
7 papers · 1 filter
Stein Variational Gradient Descent With Matrix-Valued Kernels
Dilin Wang, Ziyang Tang, Chandrajit Bajaj +1
Stein variational gradient descent (SVGD) is a particle-based inference algorithm that leverages gradient information for efficient approximate inference. In this work, we enhance…
Stein Variational Gradient Descent as Moment Matching
Qiang Liu, Dilin Wang
Stein variational gradient descent (SVGD) is a non-parametric inference algorithm that evolves a set of particles to fit a given distribution of interest. We analyze the non-asympt…
Stein Variational Message Passing for Continuous Graphical Models
Dilin Wang, Zhe Zeng, Qiang Liu
We propose a novel distributed inference algorithm for continuous graphical models, by extending Stein variational gradient descent (SVGD) to leverage the Markov dependency structu…
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…
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…
Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning
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…
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
Qiang Liu, Dilin Wang
We propose a general purpose variational inference algorithm that forms a natural counterpart of gradient descent for optimization. Our method iteratively transports a set of parti…