Search Results for author: Stephen Bonner

Found 23 papers, 15 papers with code

A Unified View of Relational Deep Learning for Drug Pair Scoring

3 code implementations4 Nov 2021 Benedek Rozemberczki, Stephen Bonner, Andriy Nikolov, Michael Ughetto, Sebastian Nilsson, Eliseo Papa

In recent years, numerous machine learning models which attempt to solve polypharmacy side effect identification, drug-drug interaction prediction and combination therapy design tasks have been proposed.

BIG-bench Machine Learning

A Knowledge Graph-Enhanced Tensor Factorisation Model for Discovering Drug Targets

no code implementations20 May 2021 Cheng Ye, Rowan Swiers, Stephen Bonner, Ian Barrett

We created a three dimensional data tensor consisting of 1, 048 gene targets, 860 diseases and 230, 011 evidence attributes and clinical outcomes connecting them, using data extracted from the Open Targets and PharmaProjects databases.

BIG-bench Machine Learning Drug Discovery +2

Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery

2 code implementations17 May 2021 Stephen Bonner, Ian P Barrett, Cheng Ye, Rowan Swiers, Ola Engkvist, Charles Tapley Hoyt, William L Hamilton

Knowledge Graphs (KG) and associated Knowledge Graph Embedding (KGE) models have recently begun to be explored in the context of drug discovery and have the potential to assist in key challenges such as target identification.

Drug Discovery Knowledge Graph Embedding +2

A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective

2 code implementations19 Feb 2021 Stephen Bonner, Ian P Barrett, Cheng Ye, Rowan Swiers, Ola Engkvist, Andreas Bender, Charles Tapley Hoyt, William L Hamilton

We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources.

BIG-bench Machine Learning Drug Discovery +1

Not Half Bad: Exploring Half-Precision in Graph Convolutional Neural Networks

no code implementations23 Oct 2020 John Brennan, Stephen Bonner, Amir Atapour-Abarghouei, Philip T Jackson, Boguslaw Obara, Andrew Stephen McGough

With the growing significance of graphs as an effective representation of data in numerous applications, efficient graph analysis using modern machine learning is receiving a growing level of attention.

Link Prediction

Rank over Class: The Untapped Potential of Ranking in Natural Language Processing

1 code implementation10 Sep 2020 Amir Atapour-Abarghouei, Stephen Bonner, Andrew Stephen McGough

Text classification has long been a staple within Natural Language Processing (NLP) with applications spanning across diverse areas such as sentiment analysis, recommender systems and spam detection.

General Classification Information Retrieval +5

BLOB : A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals

no code implementations28 Aug 2020 Otmane Sakhi, Stephen Bonner, David Rohde, Flavian vasile

In contrast, the organic signal is typically strong and covers most items, but is not always relevant to the recommendation task.

Recommendation Systems

Leveraging Synthetic Subject Invariant EEG Signals for Zero Calibration BCI

1 code implementation16 Jul 2020 Nik Khadijah Nik Aznan, Amir Atapour-Abarghouei, Stephen Bonner, Jason D. Connolly, Toby P. Breckon

Our approach, entitled the Subject Invariant SSVEP Generative Adversarial Network (SIS-GAN), produces synthetic EEG data from multiple SSVEP classes using a single network.

Signal Processing Image and Video Processing

Camera Bias in a Fine Grained Classification Task

no code implementations16 Jul 2020 Philip T. Jackson, Stephen Bonner, Ning Jia, Christopher Holder, Jon Stonehouse, Boguslaw Obara

We show that correlations between the camera used to acquire an image and the class label of that image can be exploited by convolutional neural networks (CNN), resulting in a model that "cheats" at an image classification task by recognizing which camera took the image and inferring the class label from the camera.

Classification General Classification +1

Reconsidering Analytical Variational Bounds for Output Layers of Deep Networks

no code implementations2 Oct 2019 Otmane Sakhi, Stephen Bonner, David Rohde, Flavian vasile

The combination of the re-parameterization trick with the use of variational auto-encoders has caused a sensation in Bayesian deep learning, allowing the training of realistic generative models of images and has considerably increased our ability to use scalable latent variable models.

Binary Classification General Classification +1

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions

1 code implementation21 Aug 2019 Stephen Bonner, Amir Atapour-Abarghouei, Philip T. Jackson, John Brennan, Ibad Kureshi, Georgios Theodoropoulos, Andrew Stephen McGough, Boguslaw Obara

Graphs have become a crucial way to represent large, complex and often temporal datasets across a wide range of scientific disciplines.

Social and Information Networks

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation

1 code implementation19 Aug 2019 Amir Atapour-Abarghouei, Stephen Bonner, Andrew Stephen McGough

In this paper, we investigate the possibility of classifying the ransomware a system is infected with simply based on a screenshot of the splash screen or the ransom note captured using a consumer camera commonly found in any modern mobile device.

Data Augmentation General Classification +1

Latent Variable Session-Based Recommendation

no code implementations pproximateinference AABI Symposium 2019 David Rohde, Stephen Bonner

An attractive feature of the latent variable approach is that, as the user continues to act, the posterior on the user's state tightens reflecting the recommender system's increased knowledge about that user.

Feature Engineering Session-Based Recommendations

Causal Embeddings for Recommendation: An Extended Abstract

no code implementations10 Apr 2019 Stephen Bonner, Flavian vasile

Recommendations are commonly used to modify user's natural behavior, for example, increasing product sales or the time spent on a website.

Domain Adaptation

Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification

1 code implementation15 Jan 2019 Nik Khadijah Nik Aznan, Amir Atapour-Abarghouei, Stephen Bonner, Jason Connolly, Noura Al Moubayed, Toby Breckon

Despite significant recent progress in the area of Brain-Computer Interface (BCI), there are numerous shortcomings associated with collecting Electroencephalography (EEG) signals in real-world environments.

Quantitative Methods Signal Processing

Predicting the Computational Cost of Deep Learning Models

1 code implementation28 Nov 2018 Daniel Justus, John Brennan, Stephen Bonner, Andrew Stephen McGough

But, also, it has the ability to predict execution times for scenarios unseen in the training data.

Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments

no code implementations19 Oct 2018 A. Stephen McGough, Matthew Forshaw, John Brennan, Noura Al Moubayed, Stephen Bonner

We demonstrate, through the use of simulation, how we can reduce this wasted energy by targeting tasks at computers less likely to be needed for primary use, predicting this idle time through machine learning.

BIG-bench Machine Learning

Style Augmentation: Data Augmentation via Style Randomization

1 code implementation14 Sep 2018 Philip T. Jackson, Amir Atapour-Abarghouei, Stephen Bonner, Toby Breckon, Boguslaw Obara

In addition to standard classification experiments, we investigate the effect of style augmentation (and data augmentation generally) on domain transfer tasks.

Classification Data Augmentation +4

RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising

1 code implementation2 Aug 2018 David Rohde, Stephen Bonner, Travis Dunlop, Flavian vasile, Alexandros Karatzoglou

Recommender Systems are becoming ubiquitous in many settings and take many forms, from product recommendation in e-commerce stores, to query suggestions in search engines, to friend recommendation in social networks.

Product Recommendation Recommendation Systems +2

Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study

2 code implementations19 Jun 2018 Stephen Bonner, Ibad Kureshi, John Brennan, Georgios Theodoropoulos, Andrew Stephen McGough, Boguslaw Obara

To explore this, we present extensive experimental evaluation from five state-of-the-art unsupervised graph embedding techniques, across a range of empirical graph datasets, measuring a selection of topological features.

Graph Embedding Graph Mining

Causal Embeddings for Recommendation

1 code implementation23 Jun 2017 Stephen Bonner, Flavian vasile

Many current applications use recommendations in order to modify the natural user behavior, such as to increase the number of sales or the time spent on a website.

Domain Adaptation

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