413 citations · 1.2k across the 20 of their papers we have counts for
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Kernel Stein Generative Modeling
Wei-Cheng Chang, Chun-Liang Li, Youssef Mroueh +1
We are interested in gradient-based Explicit Generative Modeling where samples can be derived from iterative gradient updates based on an estimate of the score function of the data…
Implicit Kernel Learning
Chun-Liang Li, Wei-Cheng Chang, Youssef Mroueh +2
Kernels are powerful and versatile tools in machine learning and statistics. Although the notion of universal kernels and characteristic kernels has been studied, kernel selection…
Kernel Change-point Detection with Auxiliary Deep Generative Models
Wei-Cheng Chang, Chun-Liang Li, Yiming Yang +1
Detecting the emergence of abrupt property changes in time series is a challenging problem. Kernel two-sample test has been studied for this task which makes fewer assumptions on t…
Annealing Gaussian into ReLU: a New Sampling Strategy for Leaky-ReLU RBM
Chun-Liang Li, Siamak Ravanbakhsh, Barnabas Poczos
Restricted Boltzmann Machine (RBM) is a bipartite graphical model that is used as the building block in energy-based deep generative models. Due to numerical stability and quantifi…
Rivalry of Two Families of Algorithms for Memory-Restricted Streaming PCA
Chun-Liang Li, Hsuan-Tien Lin, Chi-Jen Lu
We study the problem of recovering the subspace spanned by the first principal components of -dimensional data under the streaming setting, with a memory bound of . T…