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Nicolas Papernot - HomepageWelcome. This is Nicolas Papernot's homepage.
http://www.papernot.fr/
Welcome. This is Nicolas Papernot's homepage.
http://www.papernot.fr/
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Nicolas Papernot - Homepage | papernot.fr Reviews
https://papernot.fr
Welcome. This is Nicolas Papernot's homepage.
papernot.fr
Nicolas Papernot - Education
https://www.papernot.fr/en/education
Awards, Talks, Posters, and Press. State College, PA, USA). PhD and M.S. student in Computer Science and Engineering. Advisor: Professor Patrick McDaniel. Courses: Machine Learning, Computer and Network Security, Social Networks Data Mining and Analytics, Algorithms Design and Analysis, Performance Evaluation of Queuing Networks, Computer Architecture, Operating Systems Design and Construction. Ecole Centrale de Lyon. MS in Engineering Sciences. Advisor: Professor Thomas Aka. Ecole Centrale de Lyon.
Nicolas Papernot - Experience
https://www.papernot.fr/en/experience
Awards, Talks, Posters, and Press. May - August 2016. Software Engineer in Research Intern. Mountain View, CA). I was hosted by Úlfar Erlingsson. Google Research Security) and Martín Abadi. Researched privacy in machine learning by knowledge aggregation and transfer. Designed an approach that learns classi ers with di erential privacy guarantees. Open-sourced the code at https:/ github.com/tensorflow/models/tree/master/differential privacy/multiple teachers. Computer Security Graduate Research Assistant.
Nicolas Papernot - Awards, Talks, Posters, & Press
https://www.papernot.fr/en/skills
Awards, Talks, Posters, and Press. Google PhD Fellowship in Security. I am mentored by Samy Bengio. Graduate Research Assistantship (The Pennsylvania State University). Microsoft CyberSpace 2025 Essay Contest. Graduate Research Assistantship (The Pennsylvania State University). Scholarship for Exceptional Academic Achievements (McGill) [declined]. High School Diploma with High Honors (Ministry of Education, France). Adversarial Examples in Machine Learning ( AI with the Best. Jointly with Patrick McDaniel.
Nicolas Papernot - Publications
https://www.papernot.fr/en/publications-awards
Awards, Talks, Posters, and Press. Machine Learning in Adversarial Settings. Patrick McDaniel, Nicolas Papernot. IEEE Security and Privacy Magazine. On the (Statistical) Detection of Adversarial Examples. Kathrin Grosse, Praveen Manoharan, Nicolas Papernot. Michael Backes, and Patrick McDaniel. Expand / Collapse the abstract. Adversarial Attacks on Neural Network Policies. Sandy Huang, Nicolas Papernot. Ian Goodfellow, Yan Duan, Pieter Abbeel. Expand / Collapse the abstract. Expand / Collapse the abstract.
Nicolas Papernot - Homepage
https://www.papernot.fr/en/home
Awards, Talks, Posters, and Press. Cleverhans: A library for benchmarking vulnerability to adversarial examples. Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data. Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples. Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples. Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks. As a graduate student at Penn State.
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Patrick Drew McDaniel
http://www.patrickmcdaniel.org/index.html
I am a Distinguished Professor in the School of Electrical Engineering and Computer Science. At Pennsylvania State University. And a fellow of both IEEE. I am also the co-director of the Systems and Internet Infrastructure Security Laboratory ( SIIS. A research laboratory focused on the study of security in diverse network and computer environments. I am also the Program Manager (PM) and lead scientist for the newly created Cyber Security (CS) Collaborative Research Alliance (CRA). 2014), now Software En...
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Nicolas Papernot - Homepage
Awards, Talks, Posters, and Press. Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data. Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples. Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples. Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks. The Limitations of Deep Learning in Adversarial Settings. Research on deep learning security. MS in Engineering Sciences.
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