Feb. 15, 2024, 5:43 a.m. | Muthu Chidambaram, Rong Ge

cs.LG updates on arXiv.org arxiv.org

arXiv:2306.00740v3 Announce Type: replace
Abstract: Despite the impressive generalization capabilities of deep neural networks, they have been repeatedly shown to be overconfident when they are wrong. Fixing this issue is known as model calibration, and has consequently received much attention in the form of modified training schemes and post-training calibration procedures such as temperature scaling. While temperature scaling is frequently used because of its simplicity, it is often outperformed by modified training schemes. In this work, we identify a specific …

abstract arxiv attention capabilities cs.lg form issue limitations networks neural networks scaling stat.ml training type

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