generalisation

Cross Domain Generalisation in Humans and Machines

Recent advances in deep learning have produced models that far outstrip human performance in a number of domains. However, where machine learning approaches still fall far short of human-level performance is in the capacity to transfer knowledge across domains. While a human learner will happily apply knowledge acquired in one domain (e.g., mathematics) to a different domain (e.g., cooking; a vinaigrette is really just a ratio between edible fat and acid), machine learning models still struggle profoundly at such tasks. I will present a case that human intelligence might be (at least partially) usefully characterised by our ability to transfer knowledge widely, and a framework that we have developed for learning representations that support such transfer. The model is compared to current machine learning approaches.

Hippocampal networks support continual learning and generalisation

COSYNE 2022

Hippocampal networks support continual learning and generalisation

COSYNE 2022

Markov Chain Monte Carlo and detailed electrical models of neurons: correlations and generalisation

Computation with neuronal cultures: Effects of connectivity modularity on response separation and generalisation in simulations and experiments

FENS Forum 2024