1 citations · 2 across the 4 of their papers we have counts for
4 papers
Flexible task abstractions emerge in linear networks with fast and bounded units
Kai Sandbrink, Jan P. Bauer, Alexandra M. Proca +3
Animals survive in dynamic environments changing at arbitrary timescales, but such data distribution shifts are a challenge to neural networks. To adapt to change, neural systems m…
From Lazy to Rich: Exact Learning Dynamics in Deep Linear Networks
Clémentine C. J. Dominé, Nicolas Anguita, Alexandra M. Proca +4
Biological and artificial neural networks develop internal representations that enable them to perform complex tasks. In artificial networks, the effectiveness of these models reli…
Early learning of the optimal constant solution in neural networks and humans
Jirko Rubruck, Jan P. Bauer, Andrew Saxe +1
Deep neural networks learn increasingly complex functions over the course of training. Here, we show both empirically and theoretically that learning of the target function is prec…
Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning
Daniel Kunin, Allan Raventós, Clémentine Dominé +4
While the impressive performance of modern neural networks is often attributed to their capacity to efficiently extract task-relevant features from data, the mechanisms underlying…