978 citations · 1.6k across the 24 of their papers we have counts for
5 papers · 1 filter
Continual task learning in natural and artificial agents
Timo Flesch, Andrew Saxe, Christopher Summerfield
How do humans and other animals learn new tasks? A wave of brain recording studies has investigated how neural representations change during task learning, with a focus on how task…
The Neural Race Reduction: Dynamics of Abstraction in Gated Networks
Andrew M. Saxe, Shagun Sodhani, Sam Lewallen
Our theoretical understanding of deep learning has not kept pace with its empirical success. While network architecture is known to be critical, we do not yet understand its effect…
Know your audience: specializing grounded language models with listener subtraction
Aaditya K. Singh, David Ding, Andrew Saxe +2
Effective communication requires adapting to the idiosyncrasies of each communicative context--such as the common ground shared with each partner. Humans demonstrate this ability t…
Maslow's Hammer for Catastrophic Forgetting: Node Re-Use vs Node Activation
Sebastian Lee, Stefano Sarao Mannelli, Claudia Clopath +2
Continual learning - learning new tasks in sequence while maintaining performance on old tasks - remains particularly challenging for artificial neural networks. Surprisingly, the…
Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals
Timo Flesch, David G. Nagy, Andrew Saxe +1
Humans can learn several tasks in succession with minimal mutual interference but perform more poorly when trained on multiple tasks at once. The opposite is true for standard deep…