521 citations · 831 across the 6 of their papers we have counts for
14 papers · 1 filter
Large-scale graph representation learning with very deep GNNs and self-supervision
Ravichandra Addanki, Peter W. Battaglia, David Budden +8
Effectively and efficiently deploying graph neural networks (GNNs) at scale remains one of the most challenging aspects of graph representation learning. Many powerful solutions ha…
A Combinatorial Perspective on Transfer Learning
Jianan Wang, Eren Sezener, David Budden +2
Human intelligence is characterized not only by the capacity to learn complex skills, but the ability to rapidly adapt and acquire new skills within an ever-changing environment. I…
Gaussian Gated Linear Networks
David Budden, Adam Marblestone, Eren Sezener +3
We propose the Gaussian Gated Linear Network (G-GLN), an extension to the recently proposed GLN family of deep neural networks. Instead of using backpropagation to learn features,…
Online Learning in Contextual Bandits using Gated Linear Networks
Eren Sezener, Marcus Hutter, David Budden +2
We introduce a new and completely online contextual bandit algorithm called Gated Linear Contextual Bandits (GLCB). This algorithm is based on Gated Linear Networks (GLNs), a recen…
Gated Linear Networks
Joel Veness, Tor Lattimore, David Budden +8
This paper presents a new family of backpropagation-free neural architectures, Gated Linear Networks (GLNs). What distinguishes GLNs from contemporary neural networks is the distri…
Task-Relevant Adversarial Imitation Learning
Konrad Zolna, Scott Reed, Alexander Novikov +6
We show that a critical vulnerability in adversarial imitation is the tendency of discriminator networks to learn spurious associations between visual features and expert labels. W…