8 citations · 22 across the 4 of their papers we have counts for
10 papers
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…
Continual Learning in the Teacher-Student Setup: Impact of Task Similarity
Sebastian Lee, Sebastian Goldt, Andrew Saxe
Continual learning-the ability to learn many tasks in sequence-is critical for artificial learning systems. Yet standard training methods for deep networks often suffer from catast…
If deep learning is the answer, then what is the question?
Andrew Saxe, Stephanie Nelli, Christopher Summerfield
Neuroscience research is undergoing a minor revolution. Recent advances in machine learning and artificial intelligence (AI) research have opened up new ways of thinking about neur…
Generalisation dynamics of online learning in over-parameterised neural networks
Sebastian Goldt, Madhu S. Advani, Andrew M. Saxe +2
Deep neural networks achieve stellar generalisation on a variety of problems, despite often being large enough to easily fit all their training data. Here we study the generalisati…
Are Efficient Deep Representations Learnable?
Maxwell Nye, Andrew Saxe
Many theories of deep learning have shown that a deep network can require dramatically fewer resources to represent a given function compared to a shallow network. But a question r…
Minnorm training: an algorithm for training over-parameterized deep neural networks
Yamini Bansal, Madhu Advani, David D Cox +1
In this work, we propose a new training method for finding minimum weight norm solutions in over-parameterized neural networks (NNs). This method seeks to improve training speed an…