Petar Veličković

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...

0 Mehdi Azabou, et al. ∙

research

∙ 07/18/2023

Neural Priority Queues for Graph Neural Networks

Graph Neural Networks (GNNs) have shown considerable success in neural a...

0 Rishabh Jain, et al. ∙

research

∙ 07/17/2023

Latent Space Representations of Neural Algorithmic Reasoners

Neural Algorithmic Reasoning (NAR) is a research area focused on designi...

0 Vladimir V. Mirjanić, et al. ∙

research

∙ 07/08/2023

Parallel Algorithms Align with Neural Execution

Neural algorithmic reasoners are parallel processors. Teaching them sequ...

0 Valerie Engelmayer, et al. ∙

research

∙ 07/01/2023

Recursive Algorithmic Reasoning

Learning models that execute algorithms can enable us to address a key p...

0 Dulhan Jayalath, et al. ∙

research

∙ 06/27/2023

Asynchronous Algorithmic Alignment with Cocycles

State-of-the-art neural algorithmic reasoners make use of message passin...

0 Andrew Dudzik, et al. ∙

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 ...

0 Francesco Di Giovanni, et al. ∙

research

∙ 02/20/2023

Neural Algorithmic Reasoning with Causal Regularisation

Recent work on neural algorithmic reasoning has investigated the reasoni...

0 Beatrice Bevilacqua, et al. ∙

research

∙ 02/09/2023

Dual Algorithmic Reasoning

Neural Algorithmic Reasoning is an emerging area of machine learning whi...

0 Danilo Numeroso, et al. ∙

research

∙ 01/19/2023

Everything is Connected: Graph Neural Networks

In many ways, graphs are the main modality of data we receive from natur...

0 Petar Veličković, et al. ∙

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...

0 Ivana Nikoloska, et al. ∙

research

∙ 12/16/2022

Learnable Commutative Monoids for Graph Neural Networks

Graph neural networks (GNNs) have been shown to be highly sensitive to t...

0 Euan Ong, et al. ∙

research

∙ 12/07/2022

Learning Graph Search Heuristics

Searching for a path between two nodes in a graph is one of the most wel...

0 Michal Pándy, et al. ∙

research

∙ 11/29/2022

Continuous Neural Algorithmic Planners

Neural algorithmic reasoning studies the problem of learning algorithms ...

0 Yu He, et al. ∙

research

∙ 10/26/2022

Learning to Configure Computer Networks with Neural Algorithmic Reasoning

We present a new method for scaling automatic configuration of computer ...

0 Luca Beurer-Kellner, et al. ∙

research

∙ 10/06/2022

Expander Graph Propagation

Deploying graph neural networks (GNNs) on whole-graph classification or ...

0 Andreea Deac, et al. ∙

research

∙ 09/22/2022

A Generalist Neural Algorithmic Learner

The cornerstone of neural algorithmic reasoning is the ability to solve ...

6 Borja Ibarz, et al. ∙

research

∙ 06/23/2022

Affinity-Aware Graph Networks

Graph Neural Networks (GNNs) have emerged as a powerful technique for le...

8 Ameya Velingker, et al. ∙

research

∙ 06/17/2022

Sheaf Neural Networks with Connection Laplacians

A Sheaf Neural Network (SNN) is a type of Graph Neural Network (GNN) tha...

109 Federico Barbero, et al. ∙

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...

0 Dániel Unyi, et al. ∙

research

∙ 05/31/2022

The CLRS Algorithmic Reasoning Benchmark

Learning representations of algorithms is an emerging area of machine le...

0 Petar Veličković, et al. ∙

research

∙ 04/11/2022

Learning heuristics for A*

Path finding in graphs is one of the most studied classes of problems in...

0 Danilo Numeroso, et al. ∙

research

∙ 03/29/2022

Graph Neural Networks are Dynamic Programmers

Recent advances in neural algorithmic reasoning with graph neural networ...

0 Andrew Dudzik, et al. ∙

research

∙ 02/22/2022

Message passing all the way up

The message passing framework is the foundation of the immense success e...

0 Petar Veličković, et al. ∙

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...

10 Louis-Pascal A. C. Xhonneux, et al. ∙

research

∙ 10/11/2021

Neural Algorithmic Reasoners are Implicit Planners

Implicit planning has emerged as an elegant technique for combining lear...

17 Andreea Deac, et al. ∙

research

∙ 09/20/2021

Neural Distance Embeddings for Biological Sequences

The development of data-dependent heuristics and representations for bio...

38 Gabriele Corso, et al. ∙

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...

34 Matej Zečević, et al. ∙

research

∙ 08/25/2021

ETA Prediction with Graph Neural Networks in Google Maps

Travel-time prediction constitutes a task of high importance in transpor...

13 Austin Derrow-Pinion, et al. ∙

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...

0 Ravichandra Addanki, et al. ∙

research

∙ 07/15/2021

Algorithmic Concept-based Explainable Reasoning

Recent research on graph neural network (GNN) models successfully applie...

0 Dobrik Georgiev, et al. ∙

research

∙ 06/15/2021

Very Deep Graph Neural Networks Via Noise Regularisation

Graph Neural Networks (GNNs) perform learned message passing over an inp...

0 Jonathan Godwin, et al. ∙

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...

0 Alice Del Vecchio, et al. ∙

research

∙ 05/06/2021

Neural Algorithmic Reasoning

Algorithms have been fundamental to recent global technological advances...

0 Petar Veličković, et al. ∙

research

∙ 04/27/2021

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

The last decade has witnessed an experimental revolution in data science...

288 Michael M. Bronstein, et al. ∙

research

∙ 03/01/2021

Persistent Message Passing

Graph neural networks (GNNs) are a powerful inductive bias for modelling...

0 Heiko Strathmann, et al. ∙

research

∙ 02/18/2021

Combinatorial optimization and reasoning with graph neural networks

Combinatorial optimization is a well-established area in operations rese...

9 Quentin Cappart, et al. ∙

research

∙ 02/12/2021

Bootstrapped Representation Learning on Graphs

Current state-of-the-art self-supervised learning methods for graph neur...

0 Shantanu Thakoor, et al. ∙

research

∙ 01/11/2021

Predicting Patient Outcomes with Graph Representation Learning

Recent work on predicting patient outcomes in the Intensive Care Unit (I...

0 Emma Rocheteau, et al. ∙

research

∙ 11/10/2020

A step towards neural genome assembly

De novo genome assembly focuses on finding connections between a vast am...

8 Lovro Vrček, et al. ∙

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 ...

0 Jessica B. Hamrick, et al. ∙

research

∙ 10/25/2020

XLVIN: eXecuted Latent Value Iteration Nets

Value Iteration Networks (VINs) have emerged as a popular method to inco...

2 Andreea Deac, et al. ∙

research

∙ 07/24/2020

Hierachial Protein Function Prediction with Tails-GNNs

Protein function prediction may be framed as predicting subgraphs (with ...

11 Stefan Spalević, et al. ∙

research

∙ 06/11/2020

Pointer Graph Networks

Graph neural networks (GNNs) are typically applied to static graphs that...

33 Petar Veličković, et al. ∙

research

∙ 04/12/2020

Principal Neighbourhood Aggregation for Graph Nets

Graph Neural Networks (GNNs) have been shown to be effective models for ...

8 Gabriele Corso, et al. ∙

research

∙ 12/12/2019

The PlayStation Reinforcement Learning Environment (PSXLE)

We propose a new benchmark environment for evaluating Reinforcement Lear...

0 Carlos Purves, et al. ∙

research

∙ 10/23/2019

Neural Execution of Graph Algorithms

Graph Neural Networks (GNNs) are a powerful representational tool for so...

0 Petar Veličković, et al. ∙

research

∙ 05/02/2019

Drug-Drug Adverse Effect Prediction with Graph Co-Attention

Complex or co-existing diseases are commonly treated using drug combinat...

12 Andreea Deac, et al. ∙

research

∙ 04/12/2019

Spatio-Temporal Deep Graph Infomax

Spatio-temporal graphs such as traffic networks or gene regulatory syste...

0 Felix L. Opolka, et al. ∙

research

∙ 01/12/2019

ChronoMID - Cross-Modal Neural Networks for 3-D Temporal Medical Imaging Data

ChronoMID builds on the success of cross-modal convolutional neural netw...

2 Alexander G. Rakowski, et al. ∙

Petar Veličković

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