LEARNING FROM BIOLOGICAL SYSTEMS HOW TO REGULARIZE MACHINE-LEARNING

摘要

The present disclosure relates to machine-learning generalization, and in particular to techniques for regularizing machine-learning The present disclosure relates to machine-learning generalization, and in particular to techniques for regularizing machine-learning models using biological systems (e.g. brain data) to engineer machine-learning-algorithms that can generalize better. Particularly, aspects are directed to a computer implemented method that includes measuring a plurality of biological responses (e.g. neural responses to stimuli or other variables such body movements); generating data (e.g. responses to stimuli) using the predictive model which can denoise biological data and extract task relevant information; scaling and transforming these predictions (e.g. measure representational similarities between stimuli); and using the biologically derived data to regularize machine-learning-algorithms. The method is applicable in many domains of computer science and artificial intelligence such as perception, learning, memory, cognition, decision making.