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Fabrice Normandin

2 accepted papers

2022

Towards Scaling Difference Target Propagation by Learning Backprop Targets

ICML 2022spotlight

The development of biologically-plausible learning algorithms is important for understanding learning in the brain, but most of them fail to scale-up to real-world tasks, limiting their potential as explanations for learning by real brains. As such, it is important to explore learning algorithms tha…

2020

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

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…