Paper Citation Record · LEDGER

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples

As of 15 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:1908.07899.

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pith.paper-citation-record.v1
1908.07899 v1

Coverage vector

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Reference resolution

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Outbound references

Observation a695601a-9389-41aa-bbb0-e171fc2205a1 · outbound

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Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples Unresolved cited work

Reference 1

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This paper cites IEEE Access 6, 14410–14430 (2018).

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples IEEE Access 6, 14410–14430 (2018)

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This paper cites Comment on "Biologically inspired protection of deep networks from adversarial attacks".

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples Comment on "Biologically inspired protection of deep networks from adversarial attacks"

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This paper cites Defensive Distillation is Not Robust to Adversarial Examples.

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples Defensive Distillation is Not Robust to Adversarial Examples

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This paper cites (ed.): WordNet: An Electronic Lexical Database.

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples (ed.): WordNet: An Electronic Lexical Database

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This paper cites In: International Conference on Learning Representations (2015).

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples In: International Conference on Learning Representations (2015)

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This paper cites In: NIPS Deep Learning Workshop (2014).

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples In: NIPS Deep Learning Workshop (2014)

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This paper cites In: Proceedings of the Conference on Empirical Methods in Natural Language Processing.

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples In: Proceedings of the Conference on Empirical Methods in Natural Language Processing

Reference 8

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This paper cites In: Proceedings of the Conference on Empirical Methods in Natural Language Processing.

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples In: Proceedings of the Conference on Empirical Methods in Natural Language Processing

Reference 9

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This paper cites http://snap.stanford.edu/data (2014).

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples http://snap.stanford.edu/data (2014)

Reference 10

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This paper cites In: Proceedings of the International Joint Conference on Artiﬁcial Intelli- gence.

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples In: Proceedings of the International Joint Conference on Artiﬁcial Intelli- gence

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This paper cites In: IEEE International Conference on Computer Vision.

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples In: IEEE International Conference on Computer Vision

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Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples In: Proceedings of the International Con- ference on World Wide Web

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This paper cites In: International Conference on Learning Representations (2013).

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples In: International Conference on Learning Representations (2013)

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This paper cites In: Advances in Neural Information Processing Systems, pp.

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples In: Advances in Neural Information Processing Systems, pp

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Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples Unresolved cited work

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This paper cites Biologically inspired protection of deep networks from adversarial attacks.

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples Biologically inspired protection of deep networks from adversarial attacks

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This paper cites In: IEEE Symposium on Security and Privacy.

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples In: IEEE Symposium on Security and Privacy

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This paper cites Extending Defensive Distillation.

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples Extending Defensive Distillation

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Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples Towards the Science of Security and Privacy in Machine Learning

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Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples In: IEEE Winter Conference on Applications of Computer Vision

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Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples Towards Crafting Text Adversarial Samples

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This paper cites IEEE Transactions on Evolutionary Computation (2019).

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples IEEE Transactions on Evolutionary Computation (2019)

Reference 23

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This paper cites In: International Conference on Learning Representations (2014).

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples In: International Conference on Learning Representations (2014)

Reference 24

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This paper cites In: International Conference on Learning Representations (2018).

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples In: International Conference on Learning Representations (2018)

Reference 25

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This paper cites The Space of Transferable Adversarial Examples.

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples The Space of Transferable Adversarial Examples

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This paper cites Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey.

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey

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This paper cites In: Advances in Neural Information Processing Systems, pp.

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples In: Advances in Neural Information Processing Systems, pp

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This paper cites In: Proceedings of the Inter- national Joint Conference on Natural Language Processing.

Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples In: Proceedings of the Inter- national Joint Conference on Natural Language Processing

Reference 29

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Pith citing papers

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