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Relational inductive biases, deep learning, and graph networks

P. Battaglia, J. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, C. Gulcehre, F. Song, A. Ballard, J. Gilmer, G. Dahl, A. Vaswani, K. Allen, C. Nash, V. Langston, C. Dyer, N. Heess, D. Wierstra, P. Kohli, M. Botvinick, O. Vinyals, Y. Li, and R. Pascanu. (2018)cite arxiv:1806.01261.

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Frank Gilmer

Justin Lang

Justin Winter

Justin Möhring

Justin Vollmann

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A Fourier Perspective on Model Robustness in Computer Vision.D. Yin, R. Lopes, J. Shlens, E. Cubuk, and J. Gilmer. NeurIPS, page 13255-13265. (2019)AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty.D. Hendrycks, N. Mu, E. Cubuk, B. Zoph, J. Gilmer, and B. Lakshminarayanan. CoRR, (2019)Composition Limits and Separating Examples for Some Boolean Function Complexity Measures.J. Gilmer, M. Saks, and S. Srinivasan. CCC, page 185-196. IEEE Computer Society, (2013)Scaling Vision Transformers to 22 Billion Parameters.M. Dehghani, J. Djolonga, B. Mustafa, P. Padlewski, J. Heek, J. Gilmer, A. Steiner, M. Caron, R. Geirhos, I. Alabdulmohsin and 32 other author(s). CoRR, (2023)Small-scale proxies for large-scale Transformer training instabilities.M. Wortsman, P. Liu, L. Xiao, K. Everett, A. Alemi, B. Adlam, J. Co-Reyes, I. Gur, A. Kumar, R. Novak and 6 other author(s). ICLR, OpenReview.net, (2024)Improving Robustness Without Sacrificing Accuracy with Patch Gaussian AugmentationR. Lopes, D. Yin, B. Poole, J. Gilmer, and E. Cubuk. (2019)cite arxiv:1906.02611.Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV).B. Kim, M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viégas, and R. Sayres. ICML, volume 80 of Proceedings of Machine Learning Research, page 2673-2682. PMLR, (2018)The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization.D. Hendrycks, S. Basart, N. Mu, S. Kadavath, F. Wang, E. Dorundo, R. Desai, T. Zhu, S. Parajuli, M. Guo and 3 other author(s). ICCV, page 8320-8329. IEEE, (2021)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. AISTATS, volume 151 of Proceedings of Machine Learning Research, page 11056-11071. PMLR, (2022)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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Disambiguation of "Gilmer, Justin"

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Relational inductive biases, deep learning, and graph networks

Please choose a person to relate this publication to

Frank Gilmer

Justin Lang

Justin Winter

Justin Möhring

Justin Vollmann

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Gilmer, Justin"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Relational inductive biases, deep learning, and graph networks

Please choose a person to relate this publication to

Frank Gilmer

Justin Lang

Justin Winter

Justin Möhring

Justin Vollmann

Other publications of authors with the same name

Disambiguation

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URL
DOI
BibTeX
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Chicago
DIN 1505
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Relational inductive biases, deep learning, and graph networks