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A Loss Curvature Perspective on Training Instabilities of Deep Learning Models.

J. Gilmer, B. Ghorbani, A. Garg, S. Kudugunta, B. Neyshabur, D. Cardoze, G. Dahl, Z. Nado, and O. Firat. ICLR, OpenReview.net, (2022)

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Nado Gladi

Zachary Dotscheff

Zachary Evenson

Zachary Hayes

Zachary Taylor

Other publications of authors with the same name

Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach.S. Ariafar, J. Gilmer, Z. Nado, J. Snoek, R. Jenatton, and G. Dahl. CoRR, (2021)Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning.Z. Nado, N. Band, M. Collier, J. Djolonga, M. Dusenberry, S. Farquhar, A. Filos, M. Havasi, R. Jenatton, G. Jerfel and 14 other author(s). CoRR, (2021)Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks.S. Padhy, Z. Nado, J. Ren, J. Liu, J. Snoek, and B. Lakshminarayanan. CoRR, (2020)Pre-training helps Bayesian optimization too.Z. Wang, G. Dahl, K. Swersky, C. Lee, Z. Mariet, Z. Nado, J. Gilmer, J. Snoek, and Z. Ghahramani. CoRR, (2022)A Loss Curvature Perspective on Training Instability in Deep Learning.J. Gilmer, B. Ghorbani, A. Garg, S. Kudugunta, B. Neyshabur, D. Cardoze, G. Dahl, Z. Nado, and O. Firat. CoRR, (2021)Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks.N. Band, T. Rudner, Q. Feng, A. Filos, Z. Nado, M. Dusenberry, G. Jerfel, D. Tran, and Y. Gal. NeurIPS Datasets and Benchmarks, (2021)Underspecification Presents Challenges for Credibility in Modern Machine Learning.A. D'Amour, K. Heller, D. Moldovan, B. Adlam, B. Alipanahi, A. Beutel, C. Chen, J. Deaton, J. Eisenstein, M. Hoffman and 30 other author(s). J. Mach. Learn. Res., (2022)Adaptive Gradient Methods at the Edge of Stability.J. Cohen, B. Ghorbani, S. Krishnan, N. Agarwal, S. Medapati, M. Badura, D. Suo, D. Cardoze, Z. Nado, G. Dahl and 1 other author(s). CoRR, (2022)Benchmarking Neural Network Training Algorithms.G. Dahl, F. Schneider, Z. Nado, N. Agarwal, C. Sastry, P. Hennig, S. Medapati, R. Eschenhagen, P. Kasimbeg, D. Suo and 15 other author(s). CoRR, (2023)Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift.J. Snoek, Y. Ovadia, E. Fertig, B. Lakshminarayanan, S. Nowozin, D. Sculley, J. Dillon, J. Ren, and Z. Nado. NeurIPS, page 13969-13980. (2019)

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Disambiguation of "Nado, Zachary"

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A Loss Curvature Perspective on Training Instabilities of Deep Learning Models.

Please choose a person to relate this publication to

Nado Gladi

Zachary Dotscheff

Zachary Evenson

Zachary Hayes

Zachary Taylor

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Nado, Zachary"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML A Loss Curvature Perspective on Training Instabilities of Deep Learning Models.

Please choose a person to relate this publication to

Nado Gladi

Zachary Dotscheff

Zachary Evenson

Zachary Hayes

Zachary Taylor

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

A Loss Curvature Perspective on Training Instabilities of Deep Learning Models.