13 citations · 18 across the 20 of their papers we have counts for
7 papers · 1 filter
Predicting the Computational Cost of Deep Learning Models
Daniel Justus, John Brennan, Stephen Bonner +1
Deep learning is rapidly becoming a go-to tool for many artificial intelligence problems due to its ability to outperform other approaches and even humans at many problems. Despite…
Black-Box Autoregressive Density Estimation for State-Space Models
Tom Ryder, Andrew Golighty, A. Stephen McGough +1
State-space models (SSMs) provide a flexible framework for modelling time-series data. Consequently, SSMs are ubiquitously applied in areas such as engineering, econometrics and ep…
Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning
Stephen Bonner, John Brennan, Ibad Kureshi +3
Graphs are a commonly used construct for representing relationships between elements in complex high dimensional datasets. Many real-world phenomenon are dynamic in nature, meaning…
An Exploration of Dropout with RNNs for Natural Language Inference
Amit Gajbhiye, Sardar Jaf, Noura Al Moubayed +2
Dropout is a crucial regularization technique for the Recurrent Neural Network (RNN) models of Natural Language Inference (NLI). However, dropout has not been evaluated for the eff…
Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments
A. Stephen McGough, Matthew Forshaw, John Brennan +2
High Throughput Computing (HTC) provides a convenient mechanism for running thousands of tasks. Many HTC systems exploit computers which are provisioned for other purposes by utili…
Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study
Stephen Bonner, Ibad Kureshi, John Brennan +3
Graph embeddings have become a key and widely used technique within the field of graph mining, proving to be successful across a broad range of domains including social, citation,…