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09/07/2023
DTW+S: Shape-based Comparison of Time-series with Ordered Local Trend
Measuring distance or similarity between time-series data is a fundament...
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09/03/2023
Acoustic-to-articulatory inversion for dysarthric speech: Are pre-trained self-supervised representations favorable?
Acoustic-to-articulatory inversion (AAI) involves mapping from the acous...
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09/08/2022
Shape-based Evaluation of Epidemic Forecasts
Infectious disease forecasting for ongoing epidemics has been traditiona...
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08/09/2022
Robust Scenario Interpretation from Multi-model Prediction Efforts
Multi-model prediction efforts in infectious disease modeling and climat...
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06/17/2022
Random Forest of Epidemiological Models for Influenza Forecasting
Forecasting the hospitalizations caused by the Influenza virus is vital ...
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05/29/2022
TransforMAP: Transformer for Memory Access Prediction
Data Prefetching is a technique that can hide memory latency by fetching...
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05/01/2022
Fine-Grained Address Segmentation for Attention-Based Variable-Degree Prefetching
Machine learning algorithms have shown potential to improve prefetching ...
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03/28/2022
Design and Implementation of Knowledge Base for Runtime Management of Software Defined Hardware
Runtime-reconfigurable software coupled with reconfigurable hardware is ...
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01/19/2022
Decoupling the Depth and Scope of Graph Neural Networks
State-of-the-art Graph Neural Networks (GNNs) have limited scalability w...
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05/10/2021
Accelerating Large Scale Real-Time GNN Inference using Channel Pruning
Graph Neural Networks (GNNs) are proven to be powerful models to generat...
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02/04/2021
The EpiBench Platform to Propel AI/ML-based Epidemic Forecasting: A Prototype Demonstration Reaching Human Expert-level Performance
During the COVID-19 pandemic, a significant effort has gone into develop...
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12/02/2020
Deep Graph Neural Networks with Shallow Subgraph Samplers
While Graph Neural Networks (GNNs) are powerful models for learning repr...
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10/05/2020
Accurate, Efficient and Scalable Training of Graph Neural Networks
Graph Neural Networks (GNNs) are powerful deep learning models to genera...
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07/10/2020
Fast and Accurate Forecasting of COVID-19 Deaths Using the SIkJα Model
Forecasting the effect of COVID-19 is essential to design policies that ...
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06/03/2020
Data-driven Identification of Number of Unreported Cases for COVID-19: Bounds and Limitations
Accurate forecasts for COVID-19 are necessary for better preparedness an...
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04/23/2020
Learning to Forecast and Forecasting to Learn from the COVID-19 Pandemic
Accurate forecasts of COVID-19 is central to resource management and bui...
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03/17/2020
Towards High Performance, Portability, and Productivity: Lightweight Augmented Neural Networks for Performance Prediction
Writing high-performance code requires significant expertise in the prog...
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10/16/2019
SPEC2: SPECtral SParsE CNN Accelerator on FPGAs
To accelerate inference of Convolutional Neural Networks (CNNs), various...
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08/24/2019
Optimizing Inter-Datacenter Tail Flow Completion Times using Best Worst-case Routing
Flow routing over inter-datacenter networks is a well-known problem wher...
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07/10/2019
GraphSAINT: Graph Sampling Based Inductive Learning Method
Graph Convolutional Networks (GCNs) are powerful models for learning rep...
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10/28/2018
Accurate, Efficient and Scalable Graph Embedding
The Graph Convolutional Network (GCN) model and its variants are powerfu...
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09/29/2018
