Lucas Page-Caccia — accepted AI-conference papers

2020

Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning

Massimo Caccia, Pau Rodriguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Page-Caccia +5

NeurIPS 2020poster

Continual learning agents experience a stream of (related) tasks. The main challenge is that the agent must not forget previous tasks and also adapt to novel tasks in the stream. We are interested in the intersection of two recent continual-learning scenarios. In meta-continual learning, the model i…

Cited by 100 Source ↗PDF ↗code ↗Scholar ↗

2019

Online Continual Learning with Maximal Interfered Retrieval

Rahaf Aljundi, Eugene Belilovsky, Tinne Tuytelaars, Laurent Charlin, Massimo Caccia, Min Lin +1

NeurIPS 2019poster

Continual learning, the setting where a learning agent is faced with a never-ending stream of data, continues to be a great challenge for modern machine learning systems. In particular the online or "single-pass through the data" setting has gained attention recently as a natural setting that is dif…

Cited by 693 Source ↗PDF ↗code ↗Scholar ↗