5 citations · 5 across the 3 of their papers we have counts for
5 papers
Learning Discrete Directed Acyclic Graphs via Backpropagation
Andrew J. Wren, Pasquale Minervini, Luca Franceschi +1
Recently continuous relaxations have been proposed in order to learn Directed Acyclic Graphs (DAGs) from data by backpropagation, instead of using combinatorial optimization. Howev…
RainBench: Towards Global Precipitation Forecasting from Satellite Imagery
Christian Schroeder de Witt, Catherine Tong, Valentina Zantedeschi +5
Extreme precipitation events, such as violent rainfall and hail storms, routinely ravage economies and livelihoods around the developing world. Climate change further aggravates th…
Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting
Léo Gautheron, Pascal Germain, Amaury Habrard +3
We propose a Gradient Boosting algorithm for learning an ensemble of kernel functions adapted to the task at hand. Unlike state-of-the-art Multiple Kernel Learning techniques that…
Adversarial Robustness Toolbox v1.0.0
Maria-Irina Nicolae, Mathieu Sinn, Minh Ngoc Tran +9
Adversarial Robustness Toolbox (ART) is a Python library supporting developers and researchers in defending Machine Learning models (Deep Neural Networks, Gradient Boosted Decision…
Efficient Defenses Against Adversarial Attacks
Valentina Zantedeschi, Maria-Irina Nicolae, Ambrish Rawat
Following the recent adoption of deep neural networks (DNN) accross a wide range of applications, adversarial attacks against these models have proven to be an indisputable threat.…