3.6k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Chinese Academy of SciencesCN16 papers
- Mohamed bin Zayed University of Artificial IntelligenceAE16 papers
- Nankai UniversityCN16 papers
- ETH ZurichCH12 papers
- Tianjin UniversityCN12 papers
- University of Electronic Science and Technology of ChinaCN11 papers
- Southern University of Science and TechnologyCN9 papers
- University of AmsterdamNL9 papers
- Beijing Institute of TechnologyCN8 papers
- Wuhan UniversityCN8 papers
- Australian National UniversityAU7 papers
- Harbin Institute of TechnologyCN7 papers
6 papers · 1 filter
Kernel Continual Learning
Mohammad Mahdi Derakhshani, Xiantong Zhen, Ling Shao +1
This paper introduces kernel continual learning, a simple but effective variant of continual learning that leverages the non-parametric nature of kernel methods to tackle catastrop…
A Bit More Bayesian: Domain-Invariant Learning with Uncertainty
Zehao Xiao, Jiayi Shen, Xiantong Zhen +2
Domain generalization is challenging due to the domain shift and the uncertainty caused by the inaccessibility of target domain data. In this paper, we address both challenges with…
Conditional Generative Modeling via Learning the Latent Space
Sameera Ramasinghe, Kanchana Ranasinghe, Salman Khan +2
Although deep learning has achieved appealing results on several machine learning tasks, most of the models are deterministic at inference, limiting their application to single-mod…
On the Number of Linear Regions of Convolutional Neural Networks
H. Xiong, L. Huang, M. Yu +3
One fundamental problem in deep learning is understanding the outstanding performance of deep Neural Networks (NNs) in practice. One explanation for the superiority of NNs is that…
Learning to Learn Kernels with Variational Random Features
Xiantong Zhen, Haoliang Sun, Yingjun Du +4
In this work, we introduce kernels with random Fourier features in the meta-learning framework to leverage their strong few-shot learning ability. We propose meta variational rando…
Variational Bayesian Dropout with a Hierarchical Prior
Yuhang Liu, Wenyong Dong, Lei Zhang +2
Variational dropout (VD) is a generalization of Gaussian dropout, which aims at inferring the posterior of network weights based on a log-uniform prior on them to learn these weigh…