5 citations · 6 across the 3 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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,…