37 citations · 128 across the 32 of their papers we have counts for
Showing 2019Show all
2 papers · 1 filter
cs.CV2019★ 2 cited
Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions
Zhenyi Wang, Ping Yu, Yang Zhao +4
Human-motion generation is a long-standing challenging task due to the requirement of accurately modeling complex and diverse dynamic patterns. Most existing methods adopt sequence…
cs.LG2019★ 1 cited
KernelNet: A Data-Dependent Kernel Parameterization for Deep Generative Modeling
Yufan Zhou, Changyou Chen, Jinhui Xu
Learning with kernels is an important concept in machine learning. Standard approaches for kernel methods often use predefined kernels that require careful selection of hyperparame…