37 citations · 64 across the 10 of their papers we have counts for
6 papers · 1 filter
A Generic Approach for Enhancing GANs by Regularized Latent Optimization
Yufan Zhou, Chunyuan Li, Changyou Chen +1
With the rapidly growing model complexity and data volume, training deep generative models (DGMs) for better performance has becoming an increasingly more important challenge. Prev…
Learning High-Dimensional Distributions with Latent Neural Fokker-Planck Kernels
Yufan Zhou, Changyou Chen, Jinhui Xu
Learning high-dimensional distributions is an important yet challenging problem in machine learning with applications in various domains. In this paper, we introduce new techniques…
Meta-Learning with Neural Tangent Kernels
Yufan Zhou, Zhenyi Wang, Jiayi Xian +2
Model Agnostic Meta-Learning (MAML) has emerged as a standard framework for meta-learning, where a meta-model is learned with the ability of fast adapting to new tasks. However, as…
Learning Manifold Implicitly via Explicit Heat-Kernel Learning
Yufan Zhou, Changyou Chen, Jinhui Xu
Manifold learning is a fundamental problem in machine learning with numerous applications. Most of the existing methods directly learn the low-dimensional embedding of the data in…
Graph Neural Networks with Composite Kernels
Yufan Zhou, Jiayi Xian, Changyou Chen +1
Learning on graph structured data has drawn increasing interest in recent years. Frameworks like Graph Convolutional Networks (GCNs) have demonstrated their ability to capture stru…
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…