Andrea Cossu

According to our database, Andrea Cossu authored at least 26 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Continual Learning: Applications and the Road Forward.

Eli Verwimp, Rahaf Aljundi, Shai Ben-David, Matthias Bethge, Andrea Cossu, Alexander Gepperth, Tyler L. Hayes, Eyke Hüllermeier, Christopher Kanan, Dhireesha Kudithipudi, Christoph H. Lampert, Martin Mundt, Razvan Pascanu, Adrian Popescu, Andreas S. Tolias, Joost van de Weijer, Bing Liu, Vincenzo Lomonaco, Tinne Tuytelaars, Gido M. van de Ven

Trans. Mach. Learn. Res., 2024

Continual pre-training mitigates forgetting in language and vision.

Andrea Cossu, Antonio Carta, Lucia C. Passaro, Vincenzo Lomonaco, Tinne Tuytelaars, Davide Bacciu

Neural Networks, 2024

Drifting explanations in continual learning.

Andrea Cossu, Francesco Spinnato, Riccardo Guidotti, Davide Bacciu

Neurocomputing, 2024

Projected Latent Distillation for Data-Agnostic Consolidation in distributed continual learning.

Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, Davide Bacciu, Joost van de Weijer

Neurocomputing, 2024

MultiSTOP: Solving Functional Equations with Reinforcement Learning.

Alessandro Trenta, Davide Bacciu, Andrea Cossu, Pietro Ferrero

CoRR, 2024

Calibration of Continual Learning Models.

Lanpei Li, Elia Piccoli, Andrea Cossu, Davide Bacciu, Vincenzo Lomonaco

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Random Oscillators Network for Time Series Processing.

Andrea Ceni, Andrea Cossu, Maximilian W. Stölzle, Jingyue Liu, Cosimo Della Santina, Davide Bacciu, Claudio Gallicchio

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Deep Continual Learning (Dagstuhl Seminar 23122).

Tinne Tuytelaars, Bing Liu, Vincenzo Lomonaco, Gido van de Ven, Andrea Cossu

Dagstuhl Reports, March, 2023

Avalanche: A PyTorch Library for Deep Continual Learning.

Antonio Carta, Lorenzo Pellegrini, Andrea Cossu, Hamed Hemati, Vincenzo Lomonaco

J. Mach. Learn. Res., 2023

A Comprehensive Empirical Evaluation on Online Continual Learning.

Albin Soutif-Cormerais, Antonio Carta, Andrea Cossu, Julio Hurtado, Vincenzo Lomonaco, Joost van de Weijer, Hamed Hemati

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

A Protocol for Continual Explanation of SHAP.

Andrea Cossu, Francesco Spinnato, Riccardo Guidotti, Davide Bacciu

Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

Class-Incremental Learning with Repetition.

Hamed Hemati, Andrea Cossu, Antonio Carta, Julio Hurtado, Lorenzo Pellegrini, Davide Bacciu, Vincenzo Lomonaco, Damian Borth

Proceedings of the Conference on Lifelong Learning Agents, 2023

2022

Is Class-Incremental Enough for Continual Learning?

Andrea Cossu, Gabriele Graffieti, Lorenzo Pellegrini, Davide Maltoni, Davide Bacciu, Antonio Carta, Vincenzo Lomonaco

Frontiers Artif. Intell., 2022

Catastrophic Forgetting in Deep Graph Networks: A Graph Classification Benchmark.

Antonio Carta, Andrea Cossu, Federico Errica, Davide Bacciu

Frontiers Artif. Intell., 2022

Sample Condensation in Online Continual Learning.

Mattia Sangermano, Antonio Carta, Andrea Cossu, Davide Bacciu

Proceedings of the International Joint Conference on Neural Networks, 2022

Practical Recommendations for Replay-Based Continual Learning Methods.

Gabriele Merlin, Vincenzo Lomonaco, Andrea Cossu, Antonio Carta, Davide Bacciu

Proceedings of the Image Analysis and Processing. ICIAP 2022 Workshops, 2022

Continual Learning for Human State Monitoring.

Federico Matteoni, Andrea Cossu, Claudio Gallicchio, Vincenzo Lomonaco, Davide Bacciu

Proceedings of the 30th European Symposium on Artificial Neural Networks, 2022

Ex-Model: Continual Learning from a Stream of Trained Models.

Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, Davide Bacciu

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

2021

Continual learning for recurrent neural networks: An empirical evaluation.

Andrea Cossu, Antonio Carta, Vincenzo Lomonaco, Davide Bacciu

Neural Networks, 2021

Sustainable Artificial Intelligence through Continual Learning.

Andrea Cossu, Marta Ziosi, Vincenzo Lomonaco

CoRR, 2021

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 Amhad, Adrian Popescu, Christopher Kanan, Joost van de Weijer, Tinne Tuytelaars, Davide Bacciu, Davide Maltoni

CoRR, 2021

Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification.

Antonio Carta, Andrea Cossu, Federico Errica, Davide Bacciu

CoRR, 2021

Distilled Replay: Overcoming Forgetting Through Synthetic Samples.

Andrea Rosasco, Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, Davide Bacciu

Proceedings of the Continual Semi-Supervised Learning - First International Workshop, 2021

Continual Learning with Echo State Networks.

Andrea Cossu, Davide Bacciu, Antonio Carta, Claudio Gallicchio, Vincenzo Lomonaco

Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021

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

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

2020

Continual Learning with Gated Incremental Memories for sequential data processing.

Andrea Cossu, Antonio Carta, Davide Bacciu

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020