Mélanie Ducoffe

TL;DR: The performance of Theano is compared against Torch7 and TensorFlow on several machine learning models and recently-introduced functionalities and improvements are discussed.

TL;DR: EVA (Explaining using Verified perturbation Analysis) is introduced – the first explainability method guarantee to have an exhaustive exploration of a perturgation space and leverages the beneficial properties of verified perturbations analysis to efficiently characterize the input variables that are most likely to drive the model decision.

TL;DR: Li et al. as discussed by the authors presented a dataset-lard-of high-quality aerial images for the task of runway detection during approach and landing phases, which is composed of synthetic images but also provided manually labelled images from real landing footages, to extend the detection task to a more realistic setting.

TL;DR: In this article , the authors propose a non-monotonic space coverage approach to evaluate the monotony property using a Mixed Integer Linear Programming (MILP) solver.