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Nicolas Papernot

2014–2026 年に発表

164
論文数
30,612
被引用数
52
h 指数
104
i10 指数

被引用数

Nicolas Papernot の年別被引用数1989 年: 被引用 1 件1995 年: 被引用 1 件2000 年: 被引用 1 件2006 年: 被引用 2 件2007 年: 被引用 1 件2009 年: 被引用 3 件2010 年: 被引用 1 件2014 年: 被引用 1 件2015 年: 被引用 2 件2016 年: 被引用 64 件2017 年: 被引用 367 件2018 年: 被引用 1,190 件2019 年: 被引用 1,788 件2020 年: 被引用 2,411 件2021 年: 被引用 2,590 件2022 年: 被引用 1,926 件2023 年: 被引用 2,011 件2024 年: 被引用 1,830 件2025 年: 被引用 1,807 件2026 年: 被引用 586 件2027 年: 被引用 2 件1990〜1994 年は被引用が無いため表示していません1996〜1999 年は被引用が無いため表示していません2001〜2005 年は被引用が無いため表示していません2008 年は被引用が無いため表示していません2011〜2013 年は被引用が無いため表示していません

引用元

国・地域

この著者を引用した国・地域の世界地図アメリカ合衆国: 引用元論文 3,358 件、この内訳の 26.7%中国: 引用元論文 2,601 件、この内訳の 20.6%イギリス: 引用元論文 721 件、この内訳の 5.7%ドイツ: 引用元論文 534 件、この内訳の 4.2%オーストラリア: 引用元論文 488 件、この内訳の 3.9%カナダ: 引用元論文 463 件、この内訳の 3.7%シンガポール: 引用元論文 317 件、この内訳の 2.5%インド: 引用元論文 316 件、この内訳の 2.5%イタリア: 引用元論文 316 件、この内訳の 2.5%韓国: 引用元論文 303 件、この内訳の 2.4%日本: 引用元論文 257 件、この内訳の 2%フランス: 引用元論文 253 件、この内訳の 2%
0%26.7%その他 21.3%

機関

分野

  • Computer Science87.6%
  • Engineering3.4%
  • Social Sciences2.4%
  • Medicine1.6%
  • Decision Sciences0.9%
  • Biochemistry, Genetics and Molecular Biology0.8%
  • その他3.3%

トピック

  • Adversarial Robustness in Machine Learning20.7%
  • Anomaly Detection Techniques and Applications8.5%
  • Advanced Malware Detection Techniques6.4%
  • Privacy-Preserving Technologies in Data4.6%
  • Advanced Neural Network Applications3.6%
  • Network Security and Intrusion Detection3.2%
  • その他53%

共著者

全論文

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  1. The Limitations of Deep Learning in Adversarial Settings

    著者: Nicolas Papernot, Patrick D. McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, Ananthram Swami - IEEE European Symposium on Security and Privacy (EuroS&P), EuroS&P 2016 被引用: 3,920

  2. AI models collapse when trained on recursively generated data

    著者: Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Nicolas Papernot, Ross J. Anderson, Yarin Gal - Nature, Nat. 2024 被引用: 677

  3. The Curse of Recursion: Training on Generated Data Makes Models Forget

    著者: Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, Ross J. Anderson - arXiv (Cornell University), CoRR 2023 被引用: 363

  4. Ensemble Adversarial Training: Attacks and Defenses

    著者: Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, Patrick D. McDaniel - ICLR (Poster) 2018 被引用: 3,060

  5. Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples

    著者: Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow - arXiv (Cornell University), CoRR 2016 被引用: 1,425

  6. On Evaluating Adversarial Robustness

    著者: Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian J. Goodfellow, Aleksander Madry, Alexey Kurakin - arXiv (Cornell University), CoRR 2019 被引用: 747

  7. SoK: Security and Privacy in Machine Learning

    著者: Nicolas Papernot, Patrick D. McDaniel, Arunesh Sinha, Michael P. Wellman - IEEE European Symposium on Security and Privacy (EuroS&P), EuroS&P 2018 被引用: 405

  8. MixMatch: A Holistic Approach to Semi-Supervised Learning

    著者: David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel - NeurIPS 2019 被引用: 3,596

  9. Adversarial Attacks on Neural Network Policies

    著者: Sandy H. Huang, Nicolas Papernot, Ian J. Goodfellow, Yan Duan, Pieter Abbeel - ICLR (Workshop) 2017 被引用: 957

  10. Unrolling SGD: Understanding Factors Influencing Machine Unlearning

    著者: Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, Nicolas Papernot - IEEE 7th European Symposium on Security and Privacy (EuroS&P), EuroS&P 2022 被引用: 114

  11. Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning

    著者: Nicolas Papernot, Patrick D. McDaniel - arXiv (Cornell University), CoRR 2018 被引用: 404

  12. The Space of Transferable Adversarial Examples

    著者: Florian Tramèr, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, Patrick D. McDaniel - arXiv (Cornell University), CoRR 2017 被引用: 455

  13. Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

    著者: Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, Kunal Talwar - ICLR 2017 被引用: 1,157

  14. Adversarial Examples for Malware Detection

    著者: Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, Patrick D. McDaniel - Lecture notes in computer science, ESORICS (2) 2017 被引用: 553

  15. Machine Unlearning

    著者: Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, Nicolas Papernot - IEEE Symposium on Security and Privacy (SP) 2021 被引用: 598

  16. Scalable Private Learning with PATE

    著者: Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Úlfar Erlingsson - ICLR 2018 被引用: 725

  17. On the (Statistical) Detection of Adversarial Examples

    著者: Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, Patrick D. McDaniel - arXiv (Cornell University), CoRR 2017 被引用: 457

  18. Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples

    著者: Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow, Somesh Jha, Z. Berkay Celik, Ananthram Swami - on Asia Conference on Computer and Communications Security, AsiaCCS 2017 被引用: 3,484

  19. cleverhans v0.1: an adversarial machine learning library

    著者: Nicolas Papernot, Fartash Faghri, Nicholas Carlini, Ian Goodfellow, Reuben Feinman, Alexey Kurakin, Cihang Xie, Yash Sharma, T. B. Brown, Aurko Roy, Alexander Matyasko, Vahid Behzadan, Karen Hambardzumyan, Zhishuai Zhang, Yi-Lin Juang, Zhi Li, Ryan Sheatsley, Abhibhav Garg, Jonathan Uesato, Willi Gierke, Yinpeng Dong, David Berthelot, Paul Hendricks, Jonas Rauber, Rujun Long, Patrick McDaniel - arXiv (Cornell University), CoRR 2016 被引用: 488

  20. LLM Censorship: A Machine Learning Challenge or a Computer Security Problem?

    著者: David Glukhov, Ilia Shumailov, Yarin Gal, Nicolas Papernot, Vardan Papyan - arXiv (Cornell University), CoRR 2023 被引用: 58

  21. UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI

    著者: Ilia Shumailov, Jamie Hayes, Eleni Triantafillou, Guillermo Ortiz-Jiménez, Nicolas Papernot, Matthew Jagielski, Itay Yona, Heidi Howard, Eugene Bagdasaryan - arXiv (Cornell University), CoRR 2024 被引用: 52

  22. Tempered Sigmoid Activations for Deep Learning with Differential Privacy

    著者: Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, Úlfar Erlingsson - AAAI Conference on Artificial Intelligence 2021 被引用: 109

  23. Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy, Research, and Practice

    著者: A. Feder Cooper, Christopher A. Choquette-Choo, Miranda Bogen, Kevin Klyman, Matthew Jagielski, Katja Filippova, Ken Liu, Alexandra Chouldechova, Jamie Hayes, Yangsibo Huang, Eleni Triantafillou, Peter Kairouz, Nicole Mitchell, Niloofar Mireshghallah, Abigail Z. Jacobs, James Grimmelmann, Vitaly Shmatikov, Christopher De Sa, Ilia Shumailov, Andreas Terzis, Solon Barocas, Jennifer Wortman Vaughan, danah boyd, Yejin Choi, Sanmi Koyejo, Fernando A. Delgado, Percy Liang, Daniel E. Ho, Pamela Samuelson, Miles Brundage, David Bau, Seth Neel, Hanna M. Wallach, Amy Cyphert, Mark A. Lemley, Nicolas Papernot, Katherine Lee - NeurIPS 2025 被引用: 47

  24. On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping

    著者: Sanghyun Hong, Varun Chandrasekaran, Yigitcan Kaya, Tudor Dumitras, Nicolas Papernot - arXiv (Cornell University), CoRR 2020 被引用: 111