1.3k citations · 2.1k across the 12 of their papers we have counts for
6 papers · 1 filter
Pointer Graph Networks
Petar Veličković, Lars Buesing, Matthew C. Overlan +3
Graph neural networks (GNNs) are typically applied to static graphs that are assumed to be known upfront. This static input structure is often informed purely by insight of the mac…
Neural Execution of Graph Algorithms
Petar Veličković, Rex Ying, Matilde Padovano +2
Graph Neural Networks (GNNs) are a powerful representational tool for solving problems on graph-structured inputs. In almost all cases so far, however, they have been applied to di…
Been There, Done That: Meta-Learning with Episodic Recall
Samuel Ritter, Jane X. Wang, Zeb Kurth-Nelson +4
Meta-learning agents excel at rapidly learning new tasks from open-ended task distributions; yet, they forget what they learn about each task as soon as the next begins. When tasks…
Pushing the bounds of dropout
Gábor Melis, Charles Blundell, Tomáš Kočiský +3
We show that dropout training is best understood as performing MAP estimation concurrently for a family of conditional models whose objectives are themselves lower bounded by the o…
Memory-based Parameter Adaptation
Pablo Sprechmann, Siddhant M. Jayakumar, Jack W. Rae +7
Deep neural networks have excelled on a wide range of problems, from vision to language and game playing. Neural networks very gradually incorporate information into weights as the…
Weight Uncertainty in Neural Networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu +1
We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backp…