research
∙
05/25/2023
DP-SGD Without Clipping: The Lipschitz Neural Network Way
State-of-the-art approaches for training Differentially Private (DP) Dee...
share
research
∙
06/14/2022
When adversarial attacks become interpretable counterfactual explanations
We argue that, when learning a 1-Lipschitz neural network with the dual ...
research
∙
03/24/2022
Precipitaion Nowcasting using Deep Neural Network
Precipitation nowcasting is of great importance for weather forecast use...
research
∙
04/11/2021
The Many Faces of 1-Lipschitz Neural Networks
Lipschitz constrained models have been used to solve specifics deep lear...
research
∙
07/17/2020
A Hölderian backtracking method for min-max and min-min problems
We present a new algorithm to solve min-max or min-min problems out of t...
research
∙
06/11/2020
Achieving robustness in classification using optimal transport with hinge regularization
We propose a new framework for robust binary classification, with Deep N...
research
∙
04/15/2020
Dyslexia and Dysgraphia prediction: A new machine learning approach
Learning disabilities like dysgraphia, dyslexia, dyspraxia, etc. interfe...
research
∙
02/21/2020
Estimation of conditional mixture Weibull distribution with right-censored data using neural network for time-to-event analysis
In this paper, we consider survival analysis with right-censored data wh...
research
∙
12/13/2019
From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group)
This paper proposes a tentative and original survey of meeting points be...
research
∙
12/09/2019
Learning Disentangled Representations via Mutual Information Estimation
In this paper, we investigate the problem of learning disentangled repre...
research
∙
03/21/2019
Learning Disentangled Representations of Satellite Image Time Series
In this paper, we investigate how to learn a suitable representation of ...
research
∙
02/24/2014