5 citations · 5 across the 1 of their papers we have counts for
4 papers
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…
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…