125 citations · 221 across the 28 of their papers we have counts for
8 papers · 1 filter
Ex-Model: Continual Learning from a Stream of Trained Models
Antonio Carta, Andrea Cossu, Vincenzo Lomonaco +1
Learning continually from non-stationary data streams is a challenging research topic of growing popularity in the last few years. Being able to learn, adapt, and generalize contin…
Is Class-Incremental Enough for Continual Learning?
Andrea Cossu, Gabriele Graffieti, Lorenzo Pellegrini +4
The ability of a model to learn continually can be empirically assessed in different continual learning scenarios. Each scenario defines the constraints and the opportunities of th…
TEACHING -- Trustworthy autonomous cyber-physical applications through human-centred intelligence
Davide Bacciu, Siranush Akarmazyan, Eric Armengaud +32
This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment c…
Continual Learning with Echo State Networks
Andrea Cossu, Davide Bacciu, Antonio Carta +2
Continual Learning (CL) refers to a learning setup where data is non stationary and the model has to learn without forgetting existing knowledge. The study of CL for sequential pat…
Avalanche: an End-to-End Library for Continual Learning
Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu +25
Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing…
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification
Antonio Carta, Andrea Cossu, Federico Errica +1
In this work, we study the phenomenon of catastrophic forgetting in the graph representation learning scenario. The primary objective of the analysis is to understand whether class…
Distilled Replay: Overcoming Forgetting through Synthetic Samples
Andrea Rosasco, Antonio Carta, Andrea Cossu +2
Replay strategies are Continual Learning techniques which mitigate catastrophic forgetting by keeping a buffer of patterns from previous experiences, which are interleaved with new…
Continual Learning for Recurrent Neural Networks: an Empirical Evaluation
Andrea Cossu, Antonio Carta, Vincenzo Lomonaco +1
Learning continuously during all model lifetime is fundamental to deploy machine learning solutions robust to drifts in the data distribution. Advances in Continual Learning (CL) w…