March 13, 2024, 4:43 a.m. | Tom Sherborne, Naomi Saphra, Pradeep Dasigi, Hao Peng

cs.LG updates on arXiv.org arxiv.org

arXiv:2310.03646v2 Announce Type: replace
Abstract: Sharpness-aware minimization (SAM) reports improving domain generalization by reducing the loss surface curvature in the parameter space. However, generalization during fine-tuning is often more dependent on the transferability of representations in the function space. Trust-region methods (TR) target this goal by regularizing representation curvature to reduce catastrophic forgetting of pre-trained task-agnostic information while adopting task-specific skills. We consider unifying these strategies for low curvature in both parameter space and function space to improve out-of-domain (OOD) …

abstract arxiv cs.cl cs.lg domain fine-tuning function however loss reduce reports representation sam space surface trust type

Senior Data Engineer

@ Displate | Warsaw

Principal Software Engineer

@ Microsoft | Prague, Prague, Czech Republic

Sr. Global Reg. Affairs Manager

@ BASF | Research Triangle Park, NC, US, 27709-3528

Senior Robot Software Developer

@ OTTO Motors by Rockwell Automation | Kitchener, Ontario, Canada

Coop - Technical Service Hub Intern

@ Teradyne | Santiago de Queretaro, MX

Coop - Technical - Service Inside Sales Intern

@ Teradyne | Santiago de Queretaro, MX