89 citations · 328 across the 17 of their papers we have counts for
5 papers · 1 filter
Learning where to learn: Gradient sparsity in meta and continual learning
Johannes von Oswald, Dominic Zhao, Seijin Kobayashi +4
Finding neural network weights that generalize well from small datasets is difficult. A promising approach is to learn a weight initialization such that a small number of weight ch…
Credit Assignment in Neural Networks through Deep Feedback Control
Alexander Meulemans, Matilde Tristany Farinha, Javier García Ordóñez +3
The success of deep learning sparked interest in whether the brain learns by using similar techniques for assigning credit to each synaptic weight for its contribution to the netwo…
Conductance-based dendrites perform Bayes-optimal cue integration
Jakob Jordan, João Sacramento, Willem A. M. Wybo +2
A fundamental function of cortical circuits is the integration of information from different sources to form a reliable basis for behavior. While animals behave as if they optimall…
A contrastive rule for meta-learning
Nicolas Zucchet, Simon Schug, Johannes von Oswald +2
Humans and other animals are capable of improving their learning performance as they solve related tasks from a given problem domain, to the point of being able to learn from extre…
Posterior Meta-Replay for Continual Learning
Christian Henning, Maria R. Cervera, Francesco D'Angelo +6
Learning a sequence of tasks without access to i.i.d. observations is a widely studied form of continual learning (CL) that remains challenging. In principle, Bayesian learning dir…