Marc Masana

TL;DR: This work focuses on task incremental classification, where tasks arrive sequentially and are delineated by clear boundaries and study the influence of model capacity, weight decay and dropout regularization, and the order in which the tasks are presented, and qualitatively compare methods in terms of required memory, computation time and storage.

TL;DR: This work focuses on task-incremental classification, where tasks arrive in a batch-like fashion, and are delineated by clear boundaries, and studies the influence of model capacity, weight decay and dropout regularization, and the order in which the tasks are presented, to compare methods in terms of required memory, computation time and storage.

TL;DR: In this article, a network reparameterization that approximately diagonalizes the Fisher Information Matrix of the network parameters is proposed, which leads to significantly better performance on lifelong learning of sequential tasks.

TL;DR: In this article, the authors propose Avalanche, an open-source end-to-end library for continual learning research based on PyTorch, which is designed to provide a shared and collaborative codebase for fast prototyping, training, and reproducible evaluation of continual learning algorithms.

TL;DR: The monomodal and multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT17 Shared Task on Multimodal Translation ranked first for both En-De and En-Fr language pairs according to the automatic evaluation metrics METEOR and BLEU.