Topic: few-shot learning

Understanding Human ability to learn novel concepts from just a few sensory experiences is a fundamental problem in cognitive neuroscience. I will describe a recent work with Ben Sorcher and Surya Ganguli (PNAS, October 2022) in which we propose a simple, biologically plausible, and mathematically tractable neural mechanism for few-shot learning of naturalistic concepts. We posit that the concepts that can be learned from few examples are defined by tightly circumscribed manifolds in the neural firing-rate space of higher-order sensory areas. Discrimination between novel concepts is performed

In autonomous navigation, an agent's knowledge of its environment is represented as sensory inputs (states) linked together by actions. A popular approach for acquiring this knowledge is reinforcement learning (RL), where learning is stereotyped, i.e. an agent learns to react to a state with the action associated with maximum reward. Model training requires many trials and occurs offline, where the agent aims to learn generalisable reflexes rather than to reason about its surroundings actively. Thus, RL agents are inefficient to train and can exhibit inflexible behaviour. Indeed, behavioural f