2 citations · 2 across the 3 of their papers we have counts for
6 papers
Oops, I Sampled it Again: Reinterpreting Confidence Intervals in Few-Shot Learning
Raphael Lafargue, Luke Smith, Franck Vermet +4
The predominant method for computing confidence intervals (CI) in few-shot learning (FSL) is based on sampling the tasks with replacement, i.e.\ allowing the same samples to appear…
Evaluation of importance estimators in deep learning classifiers for Computed Tomography
Lennart Brocki, Wistan Marchadour, Jonas Maison +5
Deep learning has shown superb performance in detecting objects and classifying images, ensuring a great promise for analyzing medical imaging. Translating the success of deep lear…
Some Remarks on Replicated Simulated Annealing
Vincent Gripon, Matthias Löwe, Franck Vermet
Recently authors have introduced the idea of training discrete weights neural networks using a mix between classical simulated annealing and a replica ansatz known from the statist…
Towards an Intrinsic Definition of Robustness for a Classifier
Théo Giraudon, Vincent Gripon, Matthias Löwe +1
The robustness of classifiers has become a question of paramount importance in the past few years. Indeed, it has been shown that state-of-the-art deep learning architectures can e…
Multi-group Binary Choice with Social Interaction and a Random Communication Structure -- a Random Graph Approach
Matthias Löwe, Kristina Schubert, Franck Vermet
We construct and analyze a random graph model for discrete choice with social interaction and several groups of equal size. We concentrate on the case of two groups of equal sizes…
Improving Accuracy of Nonparametric Transfer Learning via Vector Segmentation
Vincent Gripon, Ghouthi B. Hacene, Matthias Löwe +1
Transfer learning using deep neural networks as feature extractors has become increasingly popular over the past few years. It allows to obtain state-of-the-art accuracy on dataset…