Search Results for author:
Found 4 papers, 2 papers with code
Attention for Causal Relationship Discovery from Biological Neural Dynamics
This paper explores the potential of the transformer models for learning Granger causality in networks with complex nonlinear dynamics at every node, as in neurobiological and biophysical networks.
GSplit: Scaling Graph Neural Network Training on Large Graphs via Split-Parallelism
Large-scale graphs with billions of edges are ubiquitous in many industries, science, and engineering fields such as recommendation systems, social graph analysis, knowledge base, material science, and biology.
In-Place Zero-Space Memory Protection for CNN
Convolutional Neural Networks (CNN) are being actively explored for safety-critical applications such as autonomous vehicles and aerospace, where it is essential to ensure the reliability of inference results in the presence of possible memory faults.
Exascale Deep Learning to Accelerate Cancer Research
Using MENNDL--an HPC-enabled software stack for neural architecture search--we generate a neural network with comparable accuracy to state-of-the-art networks on a cancer pathology dataset that is also $16\times$ faster at inference.
