Feb. 27, 2024, 5:44 a.m. | Xinyue Xu, Yi Qin, Lu Mi, Hao Wang, Xiaomeng Li

cs.LG updates on arXiv.org arxiv.org

arXiv:2401.14142v2 Announce Type: replace-cross
Abstract: Existing methods, such as concept bottleneck models (CBMs), have been successful in providing concept-based interpretations for black-box deep learning models. They typically work by predicting concepts given the input and then predicting the final class label given the predicted concepts. However, (1) they often fail to capture the high-order, nonlinear interaction between concepts, e.g., correcting a predicted concept (e.g., "yellow breast") does not help correct highly correlated concepts (e.g., "yellow belly"), leading to suboptimal final …

abstract arxiv box class concept concepts cs.ai cs.cv cs.lg deep learning energy prediction stat.ml type work

Data Scientist (m/f/x/d)

@ Symanto Research GmbH & Co. KG | Spain, Germany

Aumni - Site Reliability Engineer III - MLOPS

@ JPMorgan Chase & Co. | Salt Lake City, UT, United States

Senior Data Analyst

@ Teya | Budapest, Hungary

Technical Analyst (Data Analytics)

@ Contact Government Services | Chicago, IL

Engineer, AI/Machine Learning

@ Masimo | Irvine, CA, United States

Private Bank - Executive Director: Data Science and Client / Business Intelligence

@ JPMorgan Chase & Co. | Mumbai, Maharashtra, India