55 citations · 185 across the 12 of their papers we have counts for
6 papers · 1 filter
Data-Efficient Mutual Information Neural Estimator
Xiao Lin, Indranil Sur, Samuel A. Nastase +3
Measuring Mutual Information (MI) between high-dimensional, continuous, random variables from observed samples has wide theoretical and practical applications. Recent work, MINE (B…
Understanding Attention and Generalization in Graph Neural Networks
Boris Knyazev, Graham W. Taylor, Mohamed R. Amer
We aim to better understand attention over nodes in graph neural networks (GNNs) and identify factors influencing its effectiveness. We particularly focus on the ability of attenti…
Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules
Boris Knyazev, Xiao Lin, Mohamed R. Amer +1
Spectral Graph Convolutional Networks (GCNs) are a generalization of convolutional networks to learning on graph-structured data. Applications of spectral GCNs have been successful…
BitNet: Bit-Regularized Deep Neural Networks
Aswin Raghavan, Mohamed Amer, Sek Chai +1
We present a novel optimization strategy for training neural networks which we call "BitNet". The parameters of neural networks are usually unconstrained and have a dynamic range d…
GPU Activity Prediction using Representation Learning
Aswin Raghavan, Mohamed Amer, Timothy Shields +2
GPU activity prediction is an important and complex problem. This is due to the high level of contention among thousands of parallel threads. This problem was mostly addressed usin…
Low Precision Neural Networks using Subband Decomposition
Sek Chai, Aswin Raghavan, David Zhang +2
Large-scale deep neural networks (DNN) have been successfully used in a number of tasks from image recognition to natural language processing. They are trained using large training…