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Workshop
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Fri 15:30
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Invertible Residual Networks and a Novel Perspective on Adversarial Examples
Joern-Henrik Jacobsen
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Workshop
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Fri 8:30
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Detecting Adversarial Examples and Other Misclassifications in Neural Networks by Introspection
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Poster
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Wed 18:30
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Interpreting Adversarially Trained Convolutional Neural Networks
Tianyuan Zhang · Zhanxing Zhu
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Workshop
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Fri 13:45
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Provable Certificates for Adversarial Examples:Fitting a Ball in the Union of Polytopes
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Oral
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Wed 11:20
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The Odds are Odd: A Statistical Test for Detecting Adversarial Examples
Kevin Roth · Yannic Kilcher · Thomas Hofmann
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Poster
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Thu 18:30
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Analyzing and Improving Representations with the Soft Nearest Neighbor Loss
Nicholas Frosst · Nicolas Papernot · Geoffrey Hinton
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Poster
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Tue 18:30
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Using Pre-Training Can Improve Model Robustness and Uncertainty
Dan Hendrycks · Kimin Lee · Mantas Mazeika
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Poster
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Wed 18:30
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ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation
Yuzhe Yang · GUO ZHANG · Zhi Xu · Dina Katabi
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Poster
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Tue 18:30
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Adversarial Attacks on Node Embeddings via Graph Poisoning
Aleksandar Bojchevski · Stephan Günnemann
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Poster
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Wed 18:30
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Data Poisoning Attacks on Stochastic Bandits
Fang Liu · Ness Shroff
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Workshop
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Fri 15:30
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Keynote by Terrance Boult: The Deep Unknown: on Open-set and Adversarial Examples in Deep Learning
Terrance Boult
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Poster
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Tue 18:30
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Generalized No Free Lunch Theorem for Adversarial Robustness
Elvis Dohmatob
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