Prasanna Balaprakash

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Argonne National Laboratory

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Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization

An autotuning is an approach that explores a search space of possible im...
10/15/2020 ∙ by Xingfu Wu, et al. ∙ 0 ∙ share

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Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks

Wide area networking infrastructures (WANs), particularly science and re...
08/28/2020 ∙ by Tanwi Mallick, et al. ∙ 7 ∙ share

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Graph Neural Network Architecture Search for Molecular Property Prediction

Predicting the properties of a molecule from its structure is a challeng...
08/27/2020 ∙ by Shengli Jiang, et al. ∙ 0 ∙ share

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Meta Continual Learning via Dynamic Programming

Meta-continual learning algorithms seek to rapidly train a model when fa...
08/05/2020 ∙ by R. Krishnan, et al. ∙ 0 ∙ share

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Multilayer Neuromodulated Architectures for Memory-Constrained Online Continual Learning

We focus on the problem of how to achieve online continual learning unde...
07/16/2020 ∙ by Sandeep Madireddy, et al. ∙ 0 ∙ share

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A Gradient-Aware Search Algorithm for Constrained Markov Decision Processes

The canonical solution methodology for finite constrained Markov decisio...
05/07/2020 ∙ by Sami Khairy, et al. ∙ 14 ∙ share

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Towards On-Chip Bayesian Neuromorphic Learning

If edge devices are to be deployed to critical applications where their ...
05/05/2020 ∙ by Nathan Wycoff, et al. ∙ 0 ∙ share

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Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting

Highway traffic modeling and forecasting approaches are critical for int...
04/17/2020 ∙ by Tanwi Mallick, et al. ∙ 3 ∙ share

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Constrained Deep Reinforcement Learning for Energy Sustainable Multi-UAV based Random Access IoT Networks with NOMA

In this paper, we apply the Non-Orthogonal Multiple Access (NOMA) techni...
01/31/2020 ∙ by Sami Khairy, et al. ∙ 0 ∙ share

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Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems

Quantum computing is a computational paradigm with the potential to outp...
11/25/2019 ∙ by Sami Khairy, et al. ∙ 0 ∙ share

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Reinforcement-Learning-Based Variational Quantum Circuits Optimization for Combinatorial Problems

Quantum computing exploits basic quantum phenomena such as state superpo...
11/11/2019 ∙ by Sami Khairy, et al. ∙ 0 ∙ share

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Value-Added Chemical Discovery Using Reinforcement Learning

Computer-assisted synthesis planning aims to help chemists find better r...
11/10/2019 ∙ by Peihong Jiang, et al. ∙ 0 ∙ share

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Modular Deep Learning Analysis of Galaxy-Scale Strong Lensing Images

Strong gravitational lensing of astrophysical sources by foreground gala...
11/10/2019 ∙ by Sandeep Madireddy, et al. ∙ 0 ∙ share

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Graph-Partitioning-Based Diffusion Convolution Recurrent Neural Network for Large-Scale Traffic Forecasting

Traffic forecasting approaches are critical to developing adaptive strat...
09/24/2019 ∙ by Tanwi Mallick, et al. ∙ 0 ∙ share

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MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles

Optimal engine operation during a transient driving cycle is the key to ...
09/22/2019 ∙ by Shashi M. Aithal, et al. ∙ 0 ∙ share

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Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models

Rapid simulations of advection-dominated problems are vital for multiple...
09/18/2019 ∙ by Romit Maulik, et al. ∙ 0 ∙ share

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Balsam: Automated Scheduling and Execution of Dynamic, Data-Intensive HPC Workflows

We introduce the Balsam service to manage high-throughput task schedulin...
09/18/2019 ∙ by Michael A. Salim, et al. ∙ 0 ∙ share

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Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research

Cancer is a complex disease, the understanding and treatment of which ar...
09/01/2019 ∙ by Prasanna Balaprakash, et al. ∙ 0 ∙ share

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Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning

The ability to learn and adapt in real time is a central feature of biol...
06/04/2019 ∙ by Sandeep Madireddy, et al. ∙ 0 ∙ share

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Neuromorphic Acceleration for Approximate Bayesian Inference on Neural Networks via Permanent Dropout

As neural networks have begun performing increasingly critical tasks for...
04/29/2019 ∙ by Nathan Wycoff, et al. ∙ 0 ∙ share

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