11 citations · 13 across the 3 of their papers we have counts for
5 papers
Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients
Milad Alizadeh, Shyam A. Tailor, Luisa M Zintgraf +4
Pruning neural networks at initialization would enable us to find sparse models that retain the accuracy of the original network while consuming fewer computational resources for t…
Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplification
Shyam A. Tailor, René de Jong, Tiago Azevedo +2
In recent years graph neural network (GNN)-based approaches have become a popular strategy for processing point cloud data, regularly achieving state-of-the-art performance on a va…
A First Step Towards On-Device Monitoring of Body Sounds in the Wild
Shyam A. Tailor, Jagmohan Chauhan, Cecilia Mascolo
Body sounds provide rich information about the state of the human body and can be useful in many medical applications. Auscultation, the practice of listening to body sounds, has b…
Degree-Quant: Quantization-Aware Training for Graph Neural Networks
Shyam A. Tailor, Javier Fernandez-Marques, Nicholas D. Lane
Graph neural networks (GNNs) have demonstrated strong performance on a wide variety of tasks due to their ability to model non-uniform structured data. Despite their promise, there…
Are Accelerometers for Activity Recognition a Dead-end?
Catherine Tong, Shyam A. Tailor, Nicholas D. Lane
Accelerometer-based (and by extension other inertial sensors) research for Human Activity Recognition (HAR) is a dead-end. This sensor does not offer enough information for us to p…