36 citations · 103 across the 10 of their papers we have counts for
10 papers
Understanding and Improving Training-free Loss-based Diffusion Guidance
Yifei Shen, Xinyang Jiang, Yezhen Wang +3
Adding additional control to pretrained diffusion models has become an increasingly popular research area, with extensive applications in computer vision, reinforcement learning, a…
SPE: Symmetrical Prompt Enhancement for Fact Probing
Yiyuan Li, Tong Che, Yezhen Wang +3
Pretrained language models (PLMs) have been shown to accumulate factual knowledge during pretrainingng (Petroni et al., 2019). Recent works probe PLMs for the extent of this knowle…
Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One
Yezhen Wang, Tong Che, Bo Li +4
Autoregressive generative models are commonly used, especially for those tasks involving sequential data. They have, however, been plagued by a slew of inherent flaws due to the in…
Sparse Mixture-of-Experts are Domain Generalizable Learners
Bo Li, Yifei Shen, Jingkang Yang +5
Human visual perception can easily generalize to out-of-distributed visual data, which is far beyond the capability of modern machine learning models. Domain generalization (DG) ai…
Energy-Based Open-World Uncertainty Modeling for Confidence Calibration
Yezhen Wang, Bo Li, Tong Che +3
Confidence calibration is of great importance to the reliability of decisions made by machine learning systems. However, discriminative classifiers based on deep neural networks ar…
Invariant Information Bottleneck for Domain Generalization
Bo Li, Yifei Shen, Yezhen Wang +6
Invariant risk minimization (IRM) has recently emerged as a promising alternative for domain generalization. Nevertheless, the loss function is difficult to optimize for nonlinear…