Search Results for author:
Found 40 papers, 16 papers with code
Learning heuristics for A*
At training time, we exploit multi-task learning to learn jointly the Dijkstra's algorithm and a consistent heuristic function for the A* search algorithm.
Graph Neural Networks are Dynamic Programmers
Recent advances in neural algorithmic reasoning with graph neural networks (GNNs) are propped up by the notion of algorithmic alignment.
Message passing all the way up
The message passing framework is the foundation of the immense success enjoyed by graph neural networks (GNNs) in recent years.
Neural Algorithmic Reasoners are Implicit Planners
We find that prior approaches either assume that the environment is provided in such a tabular form -- which is highly restrictive -- or infer "local neighbourhoods" of states to run value iteration over -- for which we discover an algorithmic bottleneck effect.
Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond
We introduce “Noisy Nodes”, a very simple technique for improved training of GNNs, in which we corrupt the input graph with noise, and add a noise correcting node-level loss.
Neural Distance Embeddings for Biological Sequences
The development of data-dependent heuristics and representations for biological sequences that reflect their evolutionary distance is critical for large-scale biological research.
Relating Graph Neural Networks to Structural Causal Models
Causality can be described in terms of a structural causal model (SCM) that carries information on the variables of interest and their mechanistic relations.
ETA Prediction with Graph Neural Networks in Google Maps
Travel-time prediction constitutes a task of high importance in transportation networks, with web mapping services like Google Maps regularly serving vast quantities of travel time queries from users and enterprises alike.
Large-scale graph representation learning with very deep GNNs and self-supervision
In doing so, we demonstrate evidence of scalable self-supervised graph representation learning, and utility of very deep GNNs -- both very important open issues.
Reasoning-Modulated Representations
Neural networks leverage robust internal representations in order to generalise.
Algorithmic Concept-based Explainable Reasoning
Recent research on graph neural network (GNN) models successfully applied GNNs to classical graph algorithms and combinatorial optimisation problems.
Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond
From this observation we derive "Noisy Nodes", a simple technique in which we corrupt the input graph with noise, and add a noise correcting node-level loss.
Neural message passing for joint paratope-epitope prediction
Antibodies are proteins in the immune system which bind to antigens to detect and neutralise them.
Neural Algorithmic Reasoning
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 to another.
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods.
Bootstrapped Representation Learning on Graphs
Current state-of-the-art self-supervised learning methods for graph neural networks are based on contrastive learning.
Persistent Message Passing
Graph neural networks (GNNs) are a powerful inductive bias for modelling algorithmic reasoning procedures and data structures.
Combinatorial optimization and reasoning with graph neural networks
Combinatorial optimization is a well-established area in operations research and computer science.
Large-Scale Representation Learning on Graphs via Bootstrapping
To address these challenges, we introduce Bootstrapped Graph Latents (BGRL) - a graph representation learning method that learns by predicting alternative augmentations of the input.
Predicting Patient Outcomes with Graph Representation Learning
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 diagnoses and medications.
A step towards neural genome assembly
De novo genome assembly focuses on finding connections between a vast amount of short sequences in order to reconstruct the original genome.
On the role of planning in model-based deep reinforcement learning
These results indicate where and how to utilize planning in reinforcement learning settings, and highlight a number of open questions for future MBRL research.
XLVIN: eXecuted Latent Value Iteration Nets
Value Iteration Networks (VINs) have emerged as a popular method to incorporate planning algorithms within deep reinforcement learning, enabling performance improvements on tasks requiring long-range reasoning and understanding of environment dynamics.
Hierachial Protein Function Prediction with Tails-GNNs
Protein function prediction may be framed as predicting subgraphs (with certain closure properties) of a directed acyclic graph describing the hierarchy of protein functions.
Pointer Graph Networks
This static input structure is often informed purely by insight of the machine learning practitioner, and might not be optimal for the actual task the GNN is solving.
Principal Neighbourhood Aggregation for Graph Nets
Graph Neural Networks (GNNs) have been shown to be effective models for different predictive tasks on graph-structured data.
Ranked #3 on
Graph Classification
on CIFAR10 100k
The PlayStation Reinforcement Learning Environment (PSXLE)
We propose a new benchmark environment for evaluating Reinforcement Learning (RL) algorithms: the PlayStation Learning Environment (PSXLE), a PlayStation emulator modified to expose a simple control API that enables rich game-state representations.
Neural Execution of Graph Algorithms
Graph Neural Networks (GNNs) are a powerful representational tool for solving problems on graph-structured inputs.
Drug-Drug Adverse Effect Prediction with Graph Co-Attention
Complex or co-existing diseases are commonly treated using drug combinations, which can lead to higher risk of adverse side effects.
Spatio-Temporal Deep Graph Infomax
Spatio-temporal graphs such as traffic networks or gene regulatory systems present challenges for the existing deep learning methods due to the complexity of structural changes over time.
ChronoMID - Cross-Modal Neural Networks for 3-D Temporal Medical Imaging Data
ChronoMID builds on the success of cross-modal convolutional neural networks (X-CNNs), making the novel application of the technique to medical imaging data.
Towards Sparse Hierarchical Graph Classifiers
Recent advances in representation learning on graphs, mainly leveraging graph convolutional networks, have brought a substantial improvement on many graph-based benchmark tasks.
Deep Graph Infomax
We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner.
Ranked #43 on
Node Classification
on Citeseer
Attentive cross-modal paratope prediction
Antibodies are a critical part of the immune system, having the function of directly neutralising or tagging undesirable objects (the antigens) for future destruction.
Automatic Inference of Cross-modal Connection Topologies for X-CNNs
The base approach learns the topology in a data-driven manner, by using measurements performed on the base CNN and supplied data.
Quantifying the Effects of Enforcing Disentanglement on Variational Autoencoders
In this paper we quantify the effects of the parameter $\beta$ on the model performance and disentanglement.
Graph Attention Networks
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data
We analyse multimodal time-series data corresponding to weight, sleep and steps measurements.
XFlow: Cross-modal Deep Neural Networks for Audiovisual Classification
Our work improves on existing multimodal deep learning algorithms in two essential ways: (1) it presents a novel method for performing cross-modality (before features are learned from individual modalities) and (2) extends the previously proposed cross-connections which only transfer information between streams that process compatible data.
X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets
In this paper we propose cross-modal convolutional neural networks (X-CNNs), a novel biologically inspired type of CNN architectures, treating gradient descent-specialised CNNs as individual units of processing in a larger-scale network topology, while allowing for unconstrained information flow and/or weight sharing between analogous hidden layers of the network---thus generalising the already well-established concept of neural network ensembles (where information typically may flow only between the output layers of the individual networks).
