Prediction-error driven memory consolidation for continual learning and adaptive greenhouse models

Check my AI Transfer work submitted to Springer KI (German Journal on Artificial Intelligence, special issue on Developmental Robotics) on “Prediction error-driven memory consolidation for continual learning”, applied on data from innovative greenhouses:

Episodic memory replay and prediction-error driven consolidation are used to tackle online learning in deep recurrent neural networks. Inspired by evidences from cognitive sciences and neuroscience, memories are retained depending on their congruency with prior knowledge.
This congruency is estimated in terms of prediction errors resulting from a generative model. In particular, our framework chooses which samples to maintain in the episodic memory based on their expected contribution to the learning progress.
Different retention strategies are compared. We analyse their impact on the variance of the samples stored in the memory and on the stability/plasticity of the model.

Co-authored with Luis Miranda and Uwe Schmidt, Humboldt-Universität zu Berlin.