11 citations · 29 across the 6 of their papers we have counts for
6 papers
Understanding Prompt Tuning for V-L Models Through the Lens of Neural Collapse
Didi Zhu, Zexi Li, Min Zhang +6
Large-scale vision-language (V-L) models have demonstrated remarkable generalization capabilities for downstream tasks through prompt tuning. However, the mechanisms behind the lea…
Towards the ultimate PMT waveform analysis for neutrino and dark matter experiments
Dacheng Xu, Benda Xu, Erjin Bao +11
Photomultiplier tube (PMT) voltage waveforms are the raw data of many neutrino and dark matter experiments. Waveform analysis is the cornerstone of data processing. We evaluate the…
Heterogeneous Risk Minimization
Jiashuo Liu, Zheyuan Hu, Peng Cui +2
Machine learning algorithms with empirical risk minimization usually suffer from poor generalization performance due to the greedy exploitation of correlations among the training d…
Distributionally Robust Learning with Stable Adversarial Training
Jiashuo Liu, Zheyan Shen, Peng Cui +3
Machine learning algorithms with empirical risk minimization are vulnerable under distributional shifts due to the greedy adoption of all the correlations found in training data. T…
Stable Adversarial Learning under Distributional Shifts
Jiashuo Liu, Zheyan Shen, Peng Cui +4
Machine learning algorithms with empirical risk minimization are vulnerable under distributional shifts due to the greedy adoption of all the correlations found in training data. R…
Triple Generative Adversarial Networks
Chongxuan Li, Kun Xu, Jiashuo Liu +2
We propose a unified game-theoretical framework to perform classification and conditional image generation given limited supervision. It is formulated as a three-player minimax gam…