25 citations · 51 across the 6 of their papers we have counts for
6 papers
Projected Gradient Descent Algorithms for Solving Nonlinear Inverse Problems with Generative Priors
Zhaoqiang Liu, Jun Han
In this paper, we propose projected gradient descent (PGD) algorithms for signal estimation from noisy nonlinear measurements. We assume that the unknown -dimensional signal lie…
DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization
Chaoli Wang, Jun Han
Since 2016, we have witnessed the tremendous growth of artificial intelligence+visualization (AI+VIS) research. However, existing survey papers on AI+VIS focus on visual analytics…
Robust 1-bit Compressive Sensing with Partial Gaussian Circulant Matrices and Generative Priors
Zhaoqiang Liu, Subhroshekhar Ghosh, Jun Han +1
In 1-bit compressive sensing, each measurement is quantized to a single bit, namely the sign of a linear function of an unknown vector, and the goal is to accurately recover the ve…
Stein Variational Gradient Descent Without Gradient
Jun Han, Qiang Liu
Stein variational gradient decent (SVGD) has been shown to be a powerful approximate inference algorithm for complex distributions. However, the standard SVGD requires calculating…
Stein Variational Adaptive Importance Sampling
Jun Han, Qiang Liu
We propose a novel adaptive importance sampling algorithm which incorporates Stein variational gradient decent algorithm (SVGD) with importance sampling (IS). Our algorithm leverag…
Bootstrap Model Aggregation for Distributed Statistical Learning
Jun Han, Qiang Liu
In distributed, or privacy-preserving learning, we are often given a set of probabilistic models estimated from different local repositories, and asked to combine them into a singl…