1 citations · 2 across the 2 of their papers we have counts for
7 papers
Continual Pre-Training Mitigates Forgetting in Language and Vision
Andrea Cossu, Tinne Tuytelaars, Antonio Carta +3
Pre-trained models are nowadays a fundamental component of machine learning research. In continual learning, they are commonly used to initialize the model before training on the s…
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…