1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Beyond Interpretability: The Gains of Feature Monosemanticity on Model Robustness
Qi Zhang, Yifei Wang, Jingyi Cui +4
Deep learning models often suffer from a lack of interpretability due to polysemanticity, where individual neurons are activated by multiple unrelated semantics, resulting in uncle…
cs.LG2024★ 1 cited
Sketchy Moment Matching: Toward Fast and Provable Data Selection for Finetuning
Yijun Dong, Hoang Phan, Xiang Pan +1
We revisit data selection in a modern context of finetuning from a fundamental perspective. Extending the classical wisdom of variance minimization in low dimensions to high-dimens…
stat.ML2024
Bridging Domains with Approximately Shared Features
Ziliang Samuel Zhong, Xiang Pan, Qi Lei
Multi-source domain adaptation aims to reduce performance degradation when applying machine learning models to unseen domains. A fundamental challenge is devising the optimal strat…