20 citations · 43 across the 5 of their papers we have counts for
3 papers · 1 filter
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…
Do We Need Anisotropic Graph Neural Networks?
Shyam A. Tailor, Felix L. Opolka, Pietro Liò +1
Common wisdom in the graph neural network (GNN) community dictates that anisotropic models -- in which messages sent between nodes are a function of both the source and target node…
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…