7 citations · 8 across the 5 of their papers we have counts for
9 papers
Generative Minimization Networks: Training GANs Without Competition
Paulina Grnarova, Yannic Kilcher, Kfir Y. Levy +2
Many applications in machine learning can be framed as minimization problems and solved efficiently using gradient-based techniques. However, recent applications of generative mode…
Rethinking Neural Networks With Benford's Law
Surya Kant Sahu, Abhinav Java, Arshad Shaikh +1
Benford's Law (BL) or the Significant Digit Law defines the probability distribution of the first digit of numerical values in a data sample. This Law is observed in many naturally…
Meta Answering for Machine Reading
Benjamin Borschinger, Jordan Boyd-Graber, Christian Buck +7
We investigate a framework for machine reading, inspired by real world information-seeking problems, where a meta question answering system interacts with a black box environment.…
Adversarial Training is a Form of Data-dependent Operator Norm Regularization
Kevin Roth, Yannic Kilcher, Thomas Hofmann
We establish a theoretical link between adversarial training and operator norm regularization for deep neural networks. Specifically, we prove that -norm constrained projec…
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples
Kevin Roth, Yannic Kilcher, Thomas Hofmann
We investigate conditions under which test statistics exist that can reliably detect examples, which have been adversarially manipulated in a white-box attack. These statistics can…
The best defense is a good offense: Countering black box attacks by predicting slightly wrong labels
Yannic Kilcher, Thomas Hofmann
Black-Box attacks on machine learning models occur when an attacker, despite having no access to the inner workings of a model, can successfully craft an attack by means of model t…