12 citations · 19 across the 9 of their papers we have counts for
9 papers
Tabular foundation models for non-tabular tasks
Goran Nakerst, John Brennan, Wouter Beugeling +1
Tabular foundation models (TFMs) have recently emerged as a promising paradigm for machine learning on tabular data, offering the ability to generalize across datasets without task…
On the Complexity of Object Detection on Real-world Public Transportation Images for Social Distancing Measurement
Nik Khadijah Nik Aznan, John Brennan, Daniel Bell +2
Social distancing in public spaces has become an essential aspect in helping to reduce the impact of the COVID-19 pandemic. Exploiting recent advances in machine learning, there ha…
Not Half Bad: Exploring Half-Precision in Graph Convolutional Neural Networks
John Brennan, Stephen Bonner, Amir Atapour-Abarghouei +3
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 growi…
Gradient descent with momentum --- to accelerate or to super-accelerate?
Goran Nakerst, John Brennan, Masudul Haque
We consider gradient descent with `momentum', a widely used method for loss function minimization in machine learning. This method is often used with `Nesterov acceleration', meani…
Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions
Stephen Bonner, Amir Atapour-Abarghouei, Philip T Jackson +5
Graphs have become a crucial way to represent large, complex and often temporal datasets across a wide range of scientific disciplines. However, when graphs are used as input to ma…
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…