39 citations · 61 across the 6 of their papers we have counts for
17 papers
Why Are Conditional Generative Models Better Than Unconditional Ones?
Fan Bao, Chongxuan Li, Jiacheng Sun +1
Extensive empirical evidence demonstrates that conditional generative models are easier to train and perform better than unconditional ones by exploiting the labels of data. So do…
Memory Replay with Data Compression for Continual Learning
Liyuan Wang, Xingxing Zhang, Kuo Yang +7
Continual learning needs to overcome catastrophic forgetting of the past. Memory replay of representative old training samples has been shown as an effective solution, and achieves…
Stability and Generalization of Bilevel Programming in Hyperparameter Optimization
Fan Bao, Guoqiang Wu, Chongxuan Li +2
The (gradient-based) bilevel programming framework is widely used in hyperparameter optimization and has achieved excellent performance empirically. Previous theoretical work mainl…
Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization
Guoqiang Wu, Chongxuan Li, Kun Xu +1
(Partial) ranking loss is a commonly used evaluation measure for multi-label classification, which is usually optimized with convex surrogates for computational efficiency. Prior t…
MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering
Tsung Wei Tsai, Chongxuan Li, Jun Zhu
We present Mixture of Contrastive Experts (MiCE), a unified probabilistic clustering framework that simultaneously exploits the discriminative representations learned by contrastiv…
Implicit Normalizing Flows
Cheng Lu, Jianfei Chen, Chongxuan Li +2
Normalizing flows define a probability distribution by an explicit invertible transformation . In this work, we present implicit…