
Very Deep Graph Neural Networks Via Noise Regularisation
Graph Neural Networks (GNNs) perform learned message passing over an inp...
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Neural message passing for joint paratopeepitope prediction
Antibodies are proteins in the immune system which bind to antigens to d...
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Neural Algorithmic Reasoning
Algorithms have been fundamental to recent global technological advances...
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Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
The last decade has witnessed an experimental revolution in data science...
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Persistent Message Passing
Graph neural networks (GNNs) are a powerful inductive bias for modelling...
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Combinatorial optimization and reasoning with graph neural networks
Combinatorial optimization is a wellestablished area in operations rese...
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Bootstrapped Representation Learning on Graphs
Current stateoftheart selfsupervised learning methods for graph neur...
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Predicting Patient Outcomes with Graph Representation Learning
Recent work on predicting patient outcomes in the Intensive Care Unit (I...
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A step towards neural genome assembly
De novo genome assembly focuses on finding connections between a vast am...
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On the role of planning in modelbased deep reinforcement learning
Modelbased planning is often thought to be necessary for deep, careful ...
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XLVIN: eXecuted Latent Value Iteration Nets
Value Iteration Networks (VINs) have emerged as a popular method to inco...
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Hierachial Protein Function Prediction with TailsGNNs
Protein function prediction may be framed as predicting subgraphs (with ...
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Pointer Graph Networks
Graph neural networks (GNNs) are typically applied to static graphs that...
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Principal Neighbourhood Aggregation for Graph Nets
Graph Neural Networks (GNNs) have been shown to be effective models for ...
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The PlayStation Reinforcement Learning Environment (PSXLE)
We propose a new benchmark environment for evaluating Reinforcement Lear...
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Neural Execution of Graph Algorithms
Graph Neural Networks (GNNs) are a powerful representational tool for so...
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DrugDrug Adverse Effect Prediction with Graph CoAttention
Complex or coexisting diseases are commonly treated using drug combinat...
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SpatioTemporal Deep Graph Infomax
Spatiotemporal graphs such as traffic networks or gene regulatory syste...
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ChronoMID  CrossModal Neural Networks for 3D Temporal Medical Imaging Data
ChronoMID builds on the success of crossmodal convolutional neural netw...
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Towards Sparse Hierarchical Graph Classifiers
Recent advances in representation learning on graphs, mainly leveraging ...
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Deep Graph Infomax
We present Deep Graph Infomax (DGI), a general approach for learning nod...
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Attentive crossmodal paratope prediction
Antibodies are a critical part of the immune system, having the function...
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Automatic Inference of Crossmodal Connection Topologies for XCNNs
This paper introduces a way to learn crossmodal convolutional neural ne...
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Quantifying the Effects of Enforcing Disentanglement on Variational Autoencoders
The notion of disentangled autoencoders was proposed as an extension to ...
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Graph Attention Networks
We present graph attention networks (GATs), novel neural network archite...
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Crossmodal Recurrent Models for Weight Objective Prediction from Multimodal Timeseries Data
We analyse multimodal timeseries data corresponding to weight, sleep an...
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XFlow: 1D2D Crossmodal Deep Neural Networks for Audiovisual Classification
We propose two multimodal deep learning architectures that allow for cro...
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XCNN: Crossmodal Convolutional Neural Networks for Sparse Datasets
In this paper we propose crossmodal convolutional neural networks (XCN...
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Petar Veličković
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