309 citations · 1.2k across the 38 of their papers we have counts for
9 papers · 1 filter
Towards combinatorial invariance for Kazhdan-Lusztig polynomials
Charles Blundell, Lars Buesing, Alex Davies +2
Kazhdan-Lusztig polynomials are important and mysterious objects in representation theory. Here we present a new formula for their computation for symmetric groups based on the Bru…
How to transfer algorithmic reasoning knowledge to learn new algorithms?
Louis-Pascal A. C. Xhonneux, Andreea Deac, Petar Velickovic +1
Learning to execute algorithms is a fundamental problem that has been widely studied. Prior work~\cite{veli19neural} has shown that to enable systematic generalisation on graph alg…
Relating Graph Neural Networks to Structural Causal Models
Matej Zečević, Devendra Singh Dhami, Petar Veličković +1
Causality can be described in terms of a structural causal model (SCM) that carries information on the variables of interest and their mechanistic relations. For most processes of…
Reasoning-Modulated Representations
Petar Veličković, Matko Bošnjak, Thomas Kipf +4
Neural networks leverage robust internal representations in order to generalise. Learning them is difficult, and often requires a large training set that covers the data distributi…
Neural Algorithmic Reasoning
Petar Veličković, Charles Blundell
Algorithms have been fundamental to recent global technological advances and, in particular, they have been the cornerstone of technical advances in one field rapidly being applied…
Persistent Message Passing
Heiko Strathmann, Mohammadamin Barekatain, Charles Blundell +1
Graph neural networks (GNNs) are a powerful inductive bias for modelling algorithmic reasoning procedures and data structures. Their prowess was mainly demonstrated on tasks featur…
Combinatorial optimization and reasoning with graph neural networks
Quentin Cappart, Didier Chételat, Elias Khalil +3
Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolatio…
Large-Scale Representation Learning on Graphs via Bootstrapping
Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar +5
Self-supervised learning provides a promising path towards eliminating the need for costly label information in representation learning on graphs. However, to achieve state-of-the-…
Predicting Patient Outcomes with Graph Representation Learning
Emma Rocheteau, Catherine Tong, Petar Veličković +2
Recent work on predicting patient outcomes in the Intensive Care Unit (ICU) has focused heavily on the physiological time series data, largely ignoring sparse data such as diagnose…