Stephen Bonner

According to our database1, Stephen Bonner authored at least 34 papers between 2015 and 2022.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2022
A Knowledge Graph-Enhanced Tensor Factorisation Model for Discovering Drug Targets.
IEEE ACM Trans. Comput. Biol. Bioinform., 2022

Implications of topological imbalance for representation learning on biomedical knowledge graphs.
Briefings Bioinform., 2022

A review of biomedical datasets relating to drug discovery: a knowledge graph perspective.
Briefings Bioinform., 2022

A Unified View of Relational Deep Learning for Drug Pair Scoring.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

2021
Predicting Potential Drug Targets Using Tensor Factorisation and Knowledge Graph Embeddings.
CoRR, 2021

Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery.
CoRR, 2021

A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective.
CoRR, 2021

Camera Bias in a Fine Grained Classification Task.
Proceedings of the International Joint Conference on Neural Networks, 2021

Rank over Class: The Untapped Potential of Ranking in Natural Language Processing.
Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

2020
Advances in learning and understanding with graphs through machine learning.
PhD thesis, 2020

BLOB: A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Leveraging Synthetic Subject Invariant EEG Signals for Zero Calibration BCI.
Proceedings of the 25th International Conference on Pattern Recognition, 2020

Not Half Bad: Exploring Half-Precision in Graph Convolutional Neural Networks.
Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

2019
Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study.
Data Sci. Eng., 2019

Reconsidering Analytical Variational Bounds for Output Layers of Deep Networks.
CoRR, 2019

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation.
CoRR, 2019

Latent Variable Session-Based Recommendation.
CoRR, 2019

Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification.
Proceedings of the International Joint Conference on Neural Networks, 2019

Causal Embeddings for Recommendation: An Extended Abstract.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Coarse Annotation Refinement for Segmentation of Dot-Matrix Batchcodes.
Proceedings of the 18th IEEE International Conference On Machine Learning And Applications, 2019

Style Augmentation: Data Augmentation via Style Randomization.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

Volenti non fit injuria: Ransomware and its Victims.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

A King's Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2018
RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising.
CoRR, 2018

On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks.
Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2018

Causal embeddings for recommendation.
Proceedings of the 12th ACM Conference on Recommender Systems, 2018

Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments.
Proceedings of the Ninth International Green and Sustainable Computing Conference, 2018

Predicting the Computational Cost of Deep Learning Models.
Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018

Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning.
Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018

2017
Evaluating the quality of graph embeddings via topological feature reconstruction.
Proceedings of the 2017 IEEE International Conference on Big Data (IEEE BigData 2017), 2017

2016
GFP-X: A parallel approach to massive graph comparison using spark.
Proceedings of the 2016 IEEE International Conference on Big Data (IEEE BigData 2016), 2016

Deep topology classification: A new approach for massive graph classification.
Proceedings of the 2016 IEEE International Conference on Big Data (IEEE BigData 2016), 2016

2015
Data quality assessment and anomaly detection via map/reduce and linked data: A case study in the medical domain.
Proceedings of the 2015 IEEE International Conference on Big Data (IEEE BigData 2015), Santa Clara, CA, USA, October 29, 2015


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