Antonio Carta

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: In this article, the authors organize the literature on continuous learning for sequential data processing by providing a categorization of the contributions and a review of the benchmarks, and propose two new benchmarks for CL with sequential data based on existing datasets, whose characteristics resemble real world applications.

TL;DR: The goal of this paper is to analyze the feasibility of bike-sharing systems using Machine Learning methodologies, proposing and comparing different solutions and to envisage services able to make a prediction and infer if there is in use a bike that could be returned at the station where she/he is waiting.

TL;DR: This work proposes a Recurrent Neural Network (RNN) model for CL that is able to deal with concept drift in input distribution without forgetting previously acquired knowledge and implements and test a popular CL approach, Elastic Weight Consolidation (EWC), on top of two different types of RNNs.

TL;DR: The H2020 TEACHING project as mentioned in this paper proposes a human-centred vision leveraging the physiological, emotional, and cognitive state of the users as a driver for the adaptation and optimization of the autonomous applications.