Search Results for author:
Found 4 papers, 2 papers with code
Learning where to learn: Gradient sparsity in meta and continual learning
We find that patterned sparsity emerges from this process, with the pattern of sparsity varying on a problem-by-problem basis.
A contrastive rule for meta-learning
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 extremely limited data.
Learning where to learn
Finding neural network weights that generalize well from small datasets is difficult.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
