
Accelerating Large Scale RealTime GNN Inference using Channel Pruning
Graph Neural Networks (GNNs) are proven to be powerful models to generat...
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The EpiBench Platform to Propel AI/MLbased Epidemic Forecasting: A Prototype Demonstration Reaching Human Expertlevel Performance
During the COVID19 pandemic, a significant effort has gone into develop...
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Deep Graph Neural Networks with Shallow Subgraph Samplers
While Graph Neural Networks (GNNs) are powerful models for learning repr...
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Accurate, Efficient and Scalable Training of Graph Neural Networks
Graph Neural Networks (GNNs) are powerful deep learning models to genera...
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Fast and Accurate Forecasting of COVID19 Deaths Using the SIkJα Model
Forecasting the effect of COVID19 is essential to design policies that ...
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Datadriven Identification of Number of Unreported Cases for COVID19: Bounds and Limitations
Accurate forecasts for COVID19 are necessary for better preparedness an...
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Learning to Forecast and Forecasting to Learn from the COVID19 Pandemic
Accurate forecasts of COVID19 is central to resource management and bui...
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Towards High Performance, Portability, and Productivity: Lightweight Augmented Neural Networks for Performance Prediction
Writing highperformance code requires significant expertise in the prog...
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SPEC2: SPECtral SParsE CNN Accelerator on FPGAs
To accelerate inference of Convolutional Neural Networks (CNNs), various...
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Optimizing InterDatacenter Tail Flow Completion Times using Best Worstcase Routing
Flow routing over interdatacenter networks is a wellknown problem wher...
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GraphSAINT: Graph Sampling Based Inductive Learning Method
Graph Convolutional Networks (GCNs) are powerful models for learning rep...
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Accurate, Efficient and Scalable Graph Embedding
The Graph Convolutional Network (GCN) model and its variants are powerfu...
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On Minimizing the Completion Times of Long Flows over InterDatacenter WAN
Long flows contribute huge volumes of traffic over interdatacenter WAN....
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Ajitesh Srivastava
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