2 papers
cs.LG2026
Trapped by simplicity: When Transformers fail to learn from noisy features
Evan Peters, Ando Deng, Matheus H. Zambianco +2
Noise is ubiquitous in data used to train large language models, but it is not well understood whether these models are able to correctly generalize to inputs generated without noi…
quant-ph2025
Importance sampling for data-driven decoding of quantum error-correcting codes
Evan Peters
Data-driven decoding (DDD) - learning to decode syndromes of (quantum) error-correcting codes by learning from data - can be a difficult problem due to several atypical and poorly…