400 citations · 544 across the 18 of their papers we have counts for
7 papers · 1 filter
One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale
Fan Bao, Shen Nie, Kaiwen Xue +7
This paper proposes a unified diffusion framework (dubbed UniDiffuser) to fit all distributions relevant to a set of multi-modal data in one model. Our key insight is -- learning d…
Revisiting Discriminative vs. Generative Classifiers: Theory and Implications
Chenyu Zheng, Guoqiang Wu, Fan Bao +3
A large-scale deep model pre-trained on massive labeled or unlabeled data transfers well to downstream tasks. Linear evaluation freezes parameters in the pre-trained model and trai…
Regret Analysis for Hierarchical Experts Bandit Problem
Qihan Guo, Siwei Wang, Jun Zhu
We study an extension of standard bandit problem in which there are R layers of experts. Multi-layered experts make selections layer by layer and only the experts in the last layer…
Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Data
Wenkai Li, Cheng Feng, Ting Chen +1
Time series anomaly detection (TSAD) is an important data mining task with numerous applications in the IoT era. In recent years, a large number of deep neural network-based method…
CoSCL: Cooperation of Small Continual Learners is Stronger than a Big One
Liyuan Wang, Xingxing Zhang, Qian Li +2
Continual learning requires incremental compatibility with a sequence of tasks. However, the design of model architecture remains an open question: In general, learning all tasks w…
GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing
Zhongkai Hao, Chengyang Ying, Yinpeng Dong +3
Certified defenses such as randomized smoothing have shown promise towards building reliable machine learning systems against -norm bounded attacks. However, existing metho…