Measuring Representational Robustness of Neural Networks Through Shared 
Invariances

Nanda, Vedant; Speicher, Till; Kolling, Camila; Dickerson, John P; Gummadi, Krishna; Weller, Adrian

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Measuring Representational Robustness of Neural Networks Through Shared Invariances

Nanda, V., Speicher, T., Kolling, C., Dickerson, J. P., Gummadi, K., & Weller, A. (2022). Measuring Representational Robustness of Neural Networks Through Shared Invariances. In K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, & S. Sabato (Eds.), Proceedings of the 39th International Conference on Machine Learning (pp. 16368-16382). Retrieved from https://proceedings.mlr.press/v162/nanda22a.html.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000A-D0D4-B Version Permalink: https://hdl.handle.net/21.11116/0000-000A-D0D5-A

Genre: Conference Paper

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https://proceedings.mlr.press/v162/nanda22a/nanda22a.pdf (Publisher version) Open Access status unknown

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Creators:
Nanda, Vedant¹, Author
Speicher, Till¹, Author
Kolling, Camila², Author
Dickerson, John P², Author
Gummadi, Krishna¹, Author
Weller, Adrian², Author

Affiliations:
1Group K. Gummadi, Max Planck Institute for Software Systems, Max Planck Society, ou_2105291
2External Organizations, ou_persistent22

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Language(s): eng - English

Dates: Published Online: 2022

Publication Status: Published online

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Identifiers: BibTex Citekey: Nanda_ICML2022
URI: https://proceedings.mlr.press/v162/nanda22a.html

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Title: 39th International Conference on Machine Learning

Place of Event: Baltimore, MA, USA

Start-/End Date: 2022-07-17 - 2022-07-23

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Title: Proceedings of the 39th International Conference on Machine Learning

Abbreviation : ICML 2022

Source Genre: Proceedings

Creator(s):
Chaudhuri, Kamalika¹, Editor
Jegelka, Stefanie¹, Editor
Song, Le¹, Editor
Szepesvari, Csaba¹, Editor
Niu, Gang¹, Editor
Sabato, Sivan¹, Editor

Affiliations:
1 External Organizations, ou_persistent22

Publ. Info: -

Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 16368 - 16382 Identifier: -

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Title: Proceedings of Machine Learning Research

Abbreviation : PMLR

Source Genre: Series

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Pages: - Volume / Issue: 162 Sequence Number: - Start / End Page: - Identifier: ISSN: 1938-7228