5 citations · 16 across the 6 of their papers we have counts for
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cs.LG2019★ 4 cited
General Information Bottleneck Objectives and their Applications to Machine Learning
Sayandev Mukherjee
We view the Information Bottleneck Principle (IBP: Tishby et al., 1999; Schwartz-Ziv and Tishby, 2017) and Predictive Information Bottleneck Principle (PIBP: Still et al., 2007; Al…
cs.LG2019★ 4 cited
Machine Learning using the Variational Predictive Information Bottleneck with a Validation Set
Sayandev Mukherjee
Zellner (1988) modeled statistical inference in terms of information processing and postulated the Information Conservation Principle (ICP) between the input and output of the info…
cs.LG2018
PPD: Permutation Phase Defense Against Adversarial Examples in Deep Learning
Mehdi Jafarnia-Jahromi, Tasmin Chowdhury, Hsin-Tai Wu +1
Deep neural networks have demonstrated cutting edge performance on various tasks including classification. However, it is well known that adversarially designed imperceptible pertu…