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Second-order regression models exhibit progressive sharpening to the edge of stability.

, , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 169-195. PMLR, (2023)

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Temperature check: theory and practice for training models with softmax-cross-entropy losses., , , and . CoRR, (2020)Neglected Hessian component explains mysteries in Sharpness regularization., , and . CoRR, (2024)Second-order regression models exhibit progressive sharpening to the edge of stability., , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 169-195. PMLR, (2023)Temperature check: theory and practice for training models with softmax-cross-entropy losses., , , and . Trans. Mach. Learn. Res., (2023)Learning the gravitational force law and other analytic functions., , , and . CoRR, (2020)On the Interplay Between Stepsize Tuning and Progressive Sharpening., , and . CoRR, (2023)Gradient descent induces alignment between weights and the empirical NTK for deep non-linear networks., , and . CoRR, (2024)One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks., , , , , , and . ICLR, OpenReview.net, (2021)SAM operates far from home: eigenvalue regularization as a dynamical phenomenon., and . ICML, volume 202 of Proceedings of Machine Learning Research, page 152-168. PMLR, (2023)Second-order regression models exhibit progressive sharpening to the edge of stability., , and . CoRR, (2022)