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On double-descent in uncertainty quantification in overparametrized models
Florent Gérard Krzakala, Lenka Zdeborová, Lucas Andry Clarte, Bruno Loureiro, Bruno Loureiro
Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. Naive measures such as last-layer scores are well-known to yield overconfident estimates in the context of overparametrized neural networks. Several methods, ra ...
PMLR Proceedings of Machine Learning Research2023Gravitational wave signal from primordial magnetic fields in the Pulsar Timing Array frequency band
The NANOGrav, Parkes, European, and International Pulsar Timing Array (PTA) Collaborations have reported evidence for a common-spectrum process that can potentially correspond to a stochastic gravitational wave background (SGWB) in the 1-100 nHz frequency ...
AMER PHYSICAL SOC2022