208 citations · 1.7k across the 69 of their papers we have counts for
10 papers · 1 filter
Variable Bregman Majorization-Minimization Algorithm and its Application to Dirichlet Maximum Likelihood Estimation
Ségolène Martin, Jean-Christophe Pesquet, Gabriele Steidl +1
We propose a novel Bregman descent algorithm for minimizing a convex function that is expressed as the sum of a differentiable part (defined over an open set) and a possibly nonsmo…
Aggregated f-average Neural Network applied to Few-Shot Class Incremental Learning
Mathieu Vu, Emilie Chouzenoux, Ismail Ben Ayed +1
Ensemble learning leverages multiple models (i.e., weak learners) on a common machine learning task to enhance prediction performance. Basic ensembling approaches average the weak…
Majorization-Minimization for sparse SVMs
Alessandro Benfenati, Emilie Chouzenoux, Giorgia Franchini +5
Several decades ago, Support Vector Machines (SVMs) were introduced for performing binary classification tasks, under a supervised framework. Nowadays, they often outperform other…
Towards Practical Few-Shot Query Sets: Transductive Minimum Description Length Inference
Ségolène Martin, Malik Boudiaf, Emilie Chouzenoux +2
Standard few-shot benchmarks are often built upon simplifying assumptions on the query sets, which may not always hold in practice. In particular, for each task at testing time, th…
Efficient Bayes Inference in Neural Networks through Adaptive Importance Sampling
Yunshi Huang, Emilie Chouzenoux, Victor Elvira +1
Bayesian neural networks (BNNs) have received an increased interest in the last years. In BNNs, a complete posterior distribution of the unknown weight and bias parameters of the n…
Proximal Splitting Adversarial Attacks for Semantic Segmentation
Jérôme Rony, Jean-Christophe Pesquet, Ismail Ben Ayed
Classification has been the focal point of research on adversarial attacks, but only a few works investigate methods suited to denser prediction tasks, such as semantic segmentatio…