54 citations · 117 across the 8 of their papers we have counts for
14 papers
Spatio-Temporal Attention in Multi-Granular Brain Chronnectomes for Detection of Autism Spectrum Disorder
James Orme-Rogers, Ajitesh Srivastava
The traditional methods for detecting autism spectrum disorder (ASD) are expensive, subjective, and time-consuming, often taking years for a diagnosis, with many children growing w…
TransforMAP: Transformer for Memory Access Prediction
Pengmiao Zhang, Ajitesh Srivastava, Anant V. Nori +2
Data Prefetching is a technique that can hide memory latency by fetching data before it is needed by a program. Prefetching relies on accurate memory access prediction, to which ta…
Fine-Grained Address Segmentation for Attention-Based Variable-Degree Prefetching
Pengmiao Zhang, Ajitesh Srivastava, Anant V. Nori +2
Machine learning algorithms have shown potential to improve prefetching performance by accurately predicting future memory accesses. Existing approaches are based on the modeling o…
Decoupling the Depth and Scope of Graph Neural Networks
Hanqing Zeng, Muhan Zhang, Yinglong Xia +6
State-of-the-art Graph Neural Networks (GNNs) have limited scalability with respect to the graph and model sizes. On large graphs, increasing the model depth often means exponentia…
Accelerating Large Scale Real-Time GNN Inference using Channel Pruning
Hongkuan Zhou, Ajitesh Srivastava, Hanqing Zeng +2
Graph Neural Networks (GNNs) are proven to be powerful models to generate node embedding for downstream applications. However, due to the high computation complexity of GNN inferen…
The EpiBench Platform to Propel AI/ML-based Epidemic Forecasting: A Prototype Demonstration Reaching Human Expert-level Performance
Ajitesh Srivastava, Tianjian Xu, Viktor K. Prasanna
During the COVID-19 pandemic, a significant effort has gone into developing ML-driven epidemic forecasting techniques. However, benchmarks do not exist to claim if a new AI/ML tech…