4.6k citations · 5.5k across the 15 of their papers we have counts for
8 papers · 1 filter
Causally-guided Regularization of Graph Attention Improves Generalizability
Alexander P. Wu, Thomas Markovich, Bonnie Berger +2
Graph attention networks estimate the relational importance of node neighbors to aggregate relevant information over local neighborhoods for a prediction task. However, the inferre…
Graph Neural Networks for Link Prediction with Subgraph Sketching
Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi +5
Many Graph Neural Networks (GNNs) perform poorly compared to simple heuristics on Link Prediction (LP) tasks. This is due to limitations in expressive power such as the inability t…
Neural Temporal Point Processes For Modelling Electronic Health Records
Joseph Enguehard, Dan Busbridge, Adam Bozson +2
The modelling of Electronic Health Records (EHRs) has the potential to drive more efficient allocation of healthcare resources, enabling early intervention strategies and advancing…
Explaining Deep Learning Models with Constrained Adversarial Examples
Jonathan Moore, Nils Hammerla, Chris Watkins
Machine learning algorithms generally suffer from a problem of explainability. Given a classification result from a model, it is typically hard to determine what caused the decisio…
Multilingual Factor Analysis
Francisco Vargas, Kamen Brestnichki, Alex Papadopoulos-Korfiatis +1
In this work we approach the task of learning multilingual word representations in an offline manner by fitting a generative latent variable model to a multilingual dictionary. We…
Model Comparison for Semantic Grouping
Francisco Vargas, Kamen Brestnichki, Nils Hammerla
We introduce a probabilistic framework for quantifying the semantic similarity between two groups of embeddings. We formulate the task of semantic similarity as a model comparison…
Relational Graph Attention Networks
Dan Busbridge, Dane Sherburn, Pietro Cavallo +1
We investigate Relational Graph Attention Networks, a class of models that extends non-relational graph attention mechanisms to incorporate relational information, opening up these…
Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables
Nils Y. Hammerla, Shane Halloran, Thomas Ploetz
Human activity recognition (HAR) in ubiquitous computing is beginning to adopt deep learning to substitute for well-established analysis techniques that rely on hand-crafted featur…