G
German Ignacio Parisi
Researcher at University of Hamburg
Publications - 59
Citations - 3236
German Ignacio Parisi is an academic researcher from University of Hamburg. The author has contributed to research in topics: Artificial neural network & Deep learning. The author has an hindex of 14, co-authored 58 publications receiving 1866 citations. Previous affiliations of German Ignacio Parisi include University of Milano-Bicocca.
Papers
More filters
Journal ArticleDOI
Continual lifelong learning with neural networks: A review.
TL;DR: This review critically summarize the main challenges linked to lifelong learning for artificial learning systems and compare existing neural network approaches that alleviate, to different extents, catastrophic forgetting.
Journal ArticleDOI
Lifelong learning of human actions with deep neural network self-organization
TL;DR: A self-organizing neural architecture for incrementally learning to classify human actions from video sequences using a set of hierarchically arranged recurrent networks for the unsupervised learning of action representations with increasingly large spatiotemporal receptive fields is proposed.
Journal ArticleDOI
Lifelong Learning of Spatiotemporal Representations With Dual-Memory Recurrent Self-Organization.
TL;DR: The proposed dual-memory self-organizing architecture is evaluated on the CORe50 benchmark dataset for continuous object recognition, showing that it significantly outperform current methods of lifelong learning in three different incremental learning scenarios.
Proceedings ArticleDOI
Avalanche: an End-to-End Library for Continual Learning
Vincenzo Lomonaco,Lorenzo Pellegrini,Andrea Cossu,Antonio Carta,Gabriele Graffieti,Tyler L. Hayes,Matthias De Lange,Marc Masana,Jary Pomponi,Gido M. van de Ven,Martin Mundt,Qi She,Keiland W. Cooper,Jeremy Forest,Eden Belouadah,Simone Calderara,German Ignacio Parisi,Fabio Cuzzolin,Andreas S. Tolias,Simone Scardapane,Luca Antiga,Subutai Ahmad,Adrian Popescu,Christopher Kanan,Joost van de Weijer,Tinne Tuytelaars,Davide Bacciu,Davide Maltoni +27 more
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.
Journal ArticleDOI
Self-organizing neural integration of pose-motion features for human action recognition.
TL;DR: This work presents a neurobiologically-motivated approach to achieve noise-tolerant action recognition in real time that outperforms previous results on a dataset of full-body actions captured with a depth sensor, and ranks among the best results for a public benchmark of domestic daily actions.