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Konstantinos Panousis

2 accepted papers

2021

Local Competition and Stochasticity for Adversarial Robustness in Deep Learning

AISTATS 2021poster

This work addresses adversarial robustness in deep learning by considering deep networks with stochastic local winner-takes-all (LWTA) activations. This type of network units result in sparse representations from each model layer, as the units are organized in blocks where only one unit generates a…

Cited by 21SourcePDFScholar
2019

Nonparametric Bayesian Deep Networks with Local Competition

ICML 2019oral

The aim of this work is to enable inference of deep networks that retain high accuracy for the least possible model complexity, with the latter deduced from the data during inference. To this end, we revisit deep networks that comprise competing linear units, as opposed to nonlinear units that do no…