Andrea Cossu

Orcid: 0000-0002-4874-8830

According to our database1, Andrea Cossu authored at least 40 papers between 2020 and 2026.

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Bibliography

2026
Learning and Transferring Physical Models through Derivatives.
Trans. Mach. Learn. Res., 2026

A practical guide to streaming continual learning.
Neurocomputing, 2026

Random Unicycle Network (RUN!): supercharging harmonic oscillator networks via non-holonomic constraints.
Proceedings of the 34th European Symposium on Artificial Neural Networks, 2026

2025
CLA: Latent Alignment for Online Continual Self-Supervised Learning.
CoRR, July, 2025

Direct Feedback Alignment for Recurrent Neural Networks.
Proceedings of the High Performance Computing, 2025

Task-Agnostic Experts Composition for Continual Learning.
Proceedings of the Workshops at the Fourth International Conference on Hybrid Human-Artificial Intelligence co-located with the Fourth International Conference on Hybrid Human-Artificial Intelligence (HHAI 2025), 2025

Lifelong Evolution of Swarms.
Proceedings of the Genetic and Evolutionary Computation Conference, 2025

Don't drift away: Advances and Applications of Streaming and Continual Learning.
Proceedings of the 33rd European Symposium on Artificial Neural Networks, 2025

Replay-free Online Continual Learning with Self-Supervised MultiPatches.
Proceedings of the 33rd European Symposium on Artificial Neural Networks, 2025

2024
Continual Learning: Applications and the Road Forward.
Trans. Mach. Learn. Res., 2024

Continual pre-training mitigates forgetting in language and vision.
Neural Networks, 2024

Drifting explanations in continual learning.
Neurocomputing, 2024

Projected Latent Distillation for Data-Agnostic Consolidation in distributed continual learning.
Neurocomputing, 2024

Streaming Continual Learning for Unified Adaptive Intelligence in Dynamic Environments.
IEEE Intell. Syst., 2024

MultiSTOP: Solving Functional Equations with Reinforcement Learning.
CoRR, 2024

Enhancing Echo State Networks with Gradient-based Explainability Methods.
Proceedings of the 32nd European Symposium on Artificial Neural Networks, 2024

Towards Deep Continual Workspace Monitoring: Performance Evaluation of CL Strategies for Object Detection in Working Sites.
Proceedings of the 32nd European Symposium on Artificial Neural Networks, 2024


Calibration of Continual Learning Models.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Random Oscillators Network for Time Series Processing.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Sparse Reservoir Topologies for Physical Implementations of Random Oscillators Networks.
Proceedings of the 4th International Conference on AI-ML Systems, 2024

2023
Deep Continual Learning (Dagstuhl Seminar 23122).
Dagstuhl Reports, March, 2023

Avalanche: A PyTorch Library for Deep Continual Learning.
J. Mach. Learn. Res., 2023

A Comprehensive Empirical Evaluation on Online Continual Learning.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

A Protocol for Continual Explanation of SHAP.
Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

Class-Incremental Learning with Repetition.
Proceedings of the Conference on Lifelong Learning Agents, 2023

2022
Is Class-Incremental Enough for Continual Learning?
Frontiers Artif. Intell., 2022

Catastrophic Forgetting in Deep Graph Networks: A Graph Classification Benchmark.
Frontiers Artif. Intell., 2022

Sample Condensation in Online Continual Learning.
Proceedings of the International Joint Conference on Neural Networks, 2022

Practical Recommendations for Replay-Based Continual Learning Methods.
Proceedings of the Image Analysis and Processing. ICIAP 2022 Workshops, 2022

Continual Learning for Human State Monitoring.
Proceedings of the 30th European Symposium on Artificial Neural Networks, 2022

Ex-Model: Continual Learning from a Stream of Trained Models.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

2021
Continual learning for recurrent neural networks: An empirical evaluation.
Neural Networks, 2021

Sustainable Artificial Intelligence through Continual Learning.
CoRR, 2021

Avalanche: an End-to-End Library for Continual Learning.
CoRR, 2021

Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification.
CoRR, 2021

Distilled Replay: Overcoming Forgetting Through Synthetic Samples.
Proceedings of the Continual Semi-Supervised Learning - First International Workshop, 2021

Continual Learning with Echo State Networks.
Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021


2020
Continual Learning with Gated Incremental Memories for sequential data processing.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020


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