research
∙
08/17/2023
Half-Hop: A graph upsampling approach for slowing down message passing
Message passing neural networks have shown a lot of success on graph-str...
share
research
∙
07/18/2023
Neural Priority Queues for Graph Neural Networks
Graph Neural Networks (GNNs) have shown considerable success in neural a...
research
∙
07/17/2023
Latent Space Representations of Neural Algorithmic Reasoners
Neural Algorithmic Reasoning (NAR) is a research area focused on designi...
research
∙
07/08/2023
Parallel Algorithms Align with Neural Execution
Neural algorithmic reasoners are parallel processors. Teaching them sequ...
research
∙
07/01/2023
Recursive Algorithmic Reasoning
Learning models that execute algorithms can enable us to address a key p...
research
∙
06/27/2023
Asynchronous Algorithmic Alignment with Cocycles
State-of-the-art neural algorithmic reasoners make use of message passin...
research
∙
06/06/2023
How does over-squashing affect the power of GNNs?
Graph Neural Networks (GNNs) are the state-of-the-art model for machine ...
research
∙
02/20/2023
Neural Algorithmic Reasoning with Causal Regularisation
Recent work on neural algorithmic reasoning has investigated the reasoni...
research
∙
02/09/2023
Dual Algorithmic Reasoning
Neural Algorithmic Reasoning is an emerging area of machine learning whi...
research
∙
01/19/2023
Everything is Connected: Graph Neural Networks
In many ways, graphs are the main modality of data we receive from natur...
research
∙
01/19/2023
Time-Warping Invariant Quantum Recurrent Neural Networks via Quantum-Classical Adaptive Gating
Adaptive gating plays a key role in temporal data processing via classic...
research
∙
12/16/2022
Learnable Commutative Monoids for Graph Neural Networks
Graph neural networks (GNNs) have been shown to be highly sensitive to t...
research
∙
12/07/2022
Learning Graph Search Heuristics
Searching for a path between two nodes in a graph is one of the most wel...
research
∙
11/29/2022
Continuous Neural Algorithmic Planners
Neural algorithmic reasoning studies the problem of learning algorithms ...
research
∙
10/26/2022
Learning to Configure Computer Networks with Neural Algorithmic Reasoning
We present a new method for scaling automatic configuration of computer ...
research
∙
10/06/2022
Expander Graph Propagation
Deploying graph neural networks (GNNs) on whole-graph classification or ...
research
∙
09/22/2022
A Generalist Neural Algorithmic Learner
The cornerstone of neural algorithmic reasoning is the ability to solve ...
research
∙
06/23/2022
Affinity-Aware Graph Networks
Graph Neural Networks (GNNs) have emerged as a powerful technique for le...
research
∙
06/17/2022
Sheaf Neural Networks with Connection Laplacians
A Sheaf Neural Network (SNN) is a type of Graph Neural Network (GNN) tha...
research
∙
06/07/2022
Utility of Equivariant Message Passing in Cortical Mesh Segmentation
The automated segmentation of cortical areas has been a long-standing ch...
research
∙
05/31/2022
The CLRS Algorithmic Reasoning Benchmark
Learning representations of algorithms is an emerging area of machine le...
research
∙
04/11/2022
Learning heuristics for A*
Path finding in graphs is one of the most studied classes of problems in...
research
∙
03/29/2022
Graph Neural Networks are Dynamic Programmers
Recent advances in neural algorithmic reasoning with graph neural networ...
research
∙
02/22/2022
Message passing all the way up
The message passing framework is the foundation of the immense success e...
research
∙
10/26/2021
How to transfer algorithmic reasoning knowledge to learn new algorithms?
Learning to execute algorithms is a fundamental problem that has been wi...
research
∙
10/11/2021
Neural Algorithmic Reasoners are Implicit Planners
Implicit planning has emerged as an elegant technique for combining lear...
research
∙
09/20/2021
Neural Distance Embeddings for Biological Sequences
The development of data-dependent heuristics and representations for bio...
research
∙
09/09/2021
Relating Graph Neural Networks to Structural Causal Models
Causality can be described in terms of a structural causal model (SCM) t...
research
∙
08/25/2021
ETA Prediction with Graph Neural Networks in Google Maps
Travel-time prediction constitutes a task of high importance in transpor...
research
∙
07/20/2021
Large-scale graph representation learning with very deep GNNs and self-supervision
Effectively and efficiently deploying graph neural networks (GNNs) at sc...
research
∙
07/15/2021
Algorithmic Concept-based Explainable Reasoning
Recent research on graph neural network (GNN) models successfully applie...
research
∙
06/15/2021
Very Deep Graph Neural Networks Via Noise Regularisation
Graph Neural Networks (GNNs) perform learned message passing over an inp...
research
∙
05/31/2021
Neural message passing for joint paratope-epitope prediction
Antibodies are proteins in the immune system which bind to antigens to d...
research
∙
05/06/2021
Neural Algorithmic Reasoning
Algorithms have been fundamental to recent global technological advances...
research
∙
04/27/2021
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
The last decade has witnessed an experimental revolution in data science...
research
∙
03/01/2021
Persistent Message Passing
Graph neural networks (GNNs) are a powerful inductive bias for modelling...
research
∙
02/18/2021
Combinatorial optimization and reasoning with graph neural networks
Combinatorial optimization is a well-established area in operations rese...
research
∙
02/12/2021
Bootstrapped Representation Learning on Graphs
Current state-of-the-art self-supervised learning methods for graph neur...
research
∙
01/11/2021
Predicting Patient Outcomes with Graph Representation Learning
Recent work on predicting patient outcomes in the Intensive Care Unit (I...
research
∙
11/10/2020
A step towards neural genome assembly
De novo genome assembly focuses on finding connections between a vast am...
research
∙
11/08/2020
On the role of planning in model-based deep reinforcement learning
Model-based planning is often thought to be necessary for deep, careful ...
research
∙
10/25/2020
XLVIN: eXecuted Latent Value Iteration Nets
Value Iteration Networks (VINs) have emerged as a popular method to inco...
research
∙
07/24/2020
Hierachial Protein Function Prediction with Tails-GNNs
Protein function prediction may be framed as predicting subgraphs (with ...
research
∙
06/11/2020
Pointer Graph Networks
Graph neural networks (GNNs) are typically applied to static graphs that...
research
∙
04/12/2020
Principal Neighbourhood Aggregation for Graph Nets
Graph Neural Networks (GNNs) have been shown to be effective models for ...
research
∙
12/12/2019
The PlayStation Reinforcement Learning Environment (PSXLE)
We propose a new benchmark environment for evaluating Reinforcement Lear...
research
∙
10/23/2019
Neural Execution of Graph Algorithms
Graph Neural Networks (GNNs) are a powerful representational tool for so...
research
∙
05/02/2019
Drug-Drug Adverse Effect Prediction with Graph Co-Attention
Complex or co-existing diseases are commonly treated using drug combinat...
research
∙
04/12/2019
Spatio-Temporal Deep Graph Infomax
Spatio-temporal graphs such as traffic networks or gene regulatory syste...
research
∙
01/12/2019
