30 citations · 45 across the 5 of their papers we have counts for
8 papers
Lafite2: Few-shot Text-to-Image Generation
Yufan Zhou, Chunyuan Li, Changyou Chen +2
Text-to-image generation models have progressed considerably in recent years, which can now generate impressive realistic images from arbitrary text. Most of such models are traine…
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…
MixKD: Towards Efficient Distillation of Large-scale Language Models
Kevin J Liang, Weituo Hao, Dinghan Shen +4
Large-scale language models have recently demonstrated impressive empirical performance. Nevertheless, the improved results are attained at the price of bigger models, more power c…
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…