328 citations · 1.2k across the 57 of their papers we have counts for
9 papers · 1 filter
Deep Graph Neural Networks with Shallow Subgraph Samplers
Hanqing Zeng, Muhan Zhang, Yinglong Xia +6
While Graph Neural Networks (GNNs) are powerful models for learning representations on graphs, most state-of-the-art models do not have significant accuracy gain beyond two to thre…
DYNAMAP: Dynamic Algorithm Mapping Framework for Low Latency CNN Inference
Yuan Meng, Sanmukh Kuppannagari, Rajgopal Kannan +1
Most of the existing work on FPGA acceleration of Convolutional Neural Network (CNN) focus on employing a single strategy (algorithm, dataflow, etc.) across all the layers. Such an…
RECEIPT: REfine CoarsE-grained IndePendent Tasks for Parallel Tip decomposition of Bipartite Graphs
Kartik Lakhotia, Rajgopal Kannan, Viktor Prasanna +1
Tip decomposition is a crucial kernel for mining dense subgraphs in bipartite networks, with applications in spam detection, analysis of affiliation networks etc. It creates a hier…
Accurate, Efficient and Scalable Training of Graph Neural Networks
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava +2
Graph Neural Networks (GNNs) are powerful deep learning models to generate node embeddings on graphs. When applying deep GNNs on large graphs, it is still challenging to perform tr…
Fast and Accurate Forecasting of COVID-19 Deaths Using the SIkJ Model
Ajitesh Srivastava, Tianjian Xu, Viktor K. Prasanna
Forecasting the effect of COVID-19 is essential to design policies that may prepare us to handle the pandemic. Many methods have already been proposed, particularly, to forecast re…
Data-driven Identification of Number of Unreported Cases for COVID-19: Bounds and Limitations
Ajitesh Srivastava, Viktor K. Prasanna
Accurate forecasts for COVID-19 are necessary for better preparedness and resource management. Specifically, deciding the response over months or several months requires accurate l…
Maximum Entropy Model Rollouts: Fast Model Based Policy Optimization without Compounding Errors
Chi Zhang, Sanmukh Rao Kuppannagari, Viktor K Prasanna
Model usage is the central challenge of model-based reinforcement learning. Although dynamics model based on deep neural networks provide good generalization for single step predic…
Learning to Forecast and Forecasting to Learn from the COVID-19 Pandemic
Ajitesh Srivastava, Viktor K. Prasanna
Accurate forecasts of COVID-19 is central to resource management and building strategies to deal with the epidemic. We propose a heterogeneous infection rate model with human mobil…
Towards High Performance, Portability, and Productivity: Lightweight Augmented Neural Networks for Performance Prediction
Ajitesh Srivastava, Naifeng Zhang, Rajgopal Kannan +1
Writing high-performance code requires significant expertise in the programming language, compiler optimizations, and hardware knowledge. This often leads to poor productivity and…