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07/20/2023
Adversarial attacks for mixtures of classifiers
Mixtures of classifiers (a.k.a. randomized ensembles) have been proposed...
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05/30/2023
Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows
Achieving a balance between image quality (precision) and diversity (rec...
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02/14/2023
Randomization for adversarial robustness: the Good, the Bad and the Ugly
Deep neural networks are known to be vulnerable to adversarial attacks: ...
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02/01/2023
Training Normalizing Flows with the Precision-Recall Divergence
Generative models can have distinct mode of failures like mode dropping ...
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07/15/2021
On the expressivity of bi-Lipschitz normalizing flows
An invertible function is bi-Lipschitz if both the function and its inve...
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12/04/2020
Advocating for Multiple Defense Strategies against Adversarial Examples
It has been empirically observed that defense mechanisms designed to pro...
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06/15/2020
Fast Accurate Method for Bounding the Singular Values of Convolutional Layers with Application to Lipschitz Regularization
This paper tackles the problem of Lipschitz regularization of Convolutio...
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03/25/2019
Robust Neural Networks using Randomized Adversarial Training
Since the discovery of adversarial examples in machine learning, researc...
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01/29/2019
On the Expressive Power of Deep Fully Circulant Neural Networks
In this paper, we study deep fully circulant neural networks, that is de...
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10/02/2018
Training compact deep learning models for video classification using circulant matrices
In real world scenarios, model accuracy is hardly the only factor to con...
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09/11/2017
Expert Opinion Extraction from a Biomedical Database
In this paper, we tackle the problem of extracting frequent opinions fro...
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04/01/2016
