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Found 10 papers, 6 papers with code
Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey
Deep Reinforcement Learning (DRL) is an approach for training autonomous agents across various complex environments.
Riemannian data-dependent randomized smoothing for neural networks certification
Certification of neural networks is an important and challenging problem that has been attracting the attention of the machine learning community since few years.
Noisy Learning for Neural ODEs Acts as a Robustness Locus Widening
We investigate the problems and challenges of evaluating the robustness of Differential Equation-based (DE) networks against synthetic distribution shifts.
Realization Theory Of Recurrent Neural ODEs Using Polynomial System Embeddings
In this paper we show that neural ODE analogs of recurrent (ODE-RNN) and Long Short-Term Memory (ODE-LSTM) networks can be algorithmically embeddeded into the class of polynomial systems.
Improving Robustness of Deep Reinforcement Learning Agents: Environment Attack based on the Critic Network
Existing approaches of the literature to generate meaningful disturbances of the environment are adversarial reinforcement learning methods.
Stochastic sparse adversarial attacks
This paper introduces stochastic sparse adversarial attacks (SSAA), standing as simple, fast and purely noise-based targeted and untargeted attacks of neural network classifiers (NNC).
Probabilistic Jacobian-based Saliency Maps Attacks
Neural network classifiers (NNCs) are known to be vulnerable to malicious adversarial perturbations of inputs including those modifying a small fraction of the input features named sparse or $L_0$ attacks.
Geomstats: A Python Package for Riemannian Geometry in Machine Learning
We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear manifolds, such as hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, and many more.
FRSign: A Large-Scale Traffic Light Dataset for Autonomous Trains
In the realm of autonomous transportation, there have been many initiatives for open-sourcing self-driving cars datasets, but much less for alternative methods of transportation such as trains.
From Node Embedding To Community Embedding : A Hyperbolic Approach
Considering the success of hyperbolic representations of graph-structured data in last years, an ongoing challenge is to set up a hyperbolic approach for the community detection problem.
