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A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Samples

Research article (International Conference on Learning Representations, 2017) · cited 15× · AI/ML
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A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Samples

Summary

A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Samples is a scholarly article[1].

Key Facts

  • A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Samples's instance of is recorded as scholarly article[2].

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Samples. Retrieved May 24, 2026, from https://4ort.xyz/entity/a-theoretical-framework-for-robustness-of-deep-classifiers-against-adversarial-samples
MLA “A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Samples.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/a-theoretical-framework-for-robustness-of-deep-classifiers-against-adversarial-samples.
BibTeX @misc{4ortxyz_a-theoretical-framework-for-robustness-of-deep-classifiers-against-adversarial-samples_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Samples}}, year = {2026}, url = {https://4ort.xyz/entity/a-theoretical-framework-for-robustness-of-deep-classifiers-against-adversarial-samples}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Samples — https://4ort.xyz/entity/a-theoretical-framework-for-robustness-of-deep-classifiers-against-adversarial-samples (retrieved 2026-05-24)

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