3 papers
cs.LG2025
From Shortcut to Induction Head: How Data Diversity Shapes Algorithm Selection in Transformers
Ryotaro Kawata, Yujin Song, Alberto Bietti +4
Transformers can implement both generalizable algorithms (e.g., induction heads) and simple positional shortcuts (e.g., memorizing fixed output positions). In this work, we study h…
quant-ph2025
Sample-efficient quantum error mitigation via classical learning surrogates
Wei-You Liao, Ge Yan, Yujin Song +5
The pursuit of practical quantum utility on near-term quantum processors is critically challenged by their inherent noise. Quantum error mitigation (QEM) techniques are leading sol…
cs.LG2025
How Does Label Noise Gradient Descent Improve Generalization in the Low SNR Regime?
Wei Huang, Andi Han, Yujin Song +4
The capacity of deep learning models is often large enough to both learn the underlying statistical signal and overfit to noise in the training set. This noise memorization can be…