557 citations · 557 across the 3 of their papers we have counts for
3 papers
cs.LG2021
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…
cs.LG2019★ 557 cited
Three scenarios for continual learning
Gido M. van de Ven, Andreas S. Tolias
Standard artificial neural networks suffer from the well-known issue of catastrophic forgetting, making continual or lifelong learning difficult for machine learning. In recent yea…
cs.LG2018
Generative replay with feedback connections as a general strategy for continual learning
Gido M. van de Ven, Andreas S. Tolias
A major obstacle to developing artificial intelligence applications capable of true lifelong learning is that artificial neural networks quickly or catastrophically forget previous…