Prasanna Balaprakash

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Argonne National Laboratory

Learning-Based Distributed Random Access for Multi-Cell IoT Networks with NOMA

Non-orthogonal multiple access (NOMA) is a key technology to enable mass...
01/02/2021 ∙ by Sami Khairy, et al. ∙ 1 ∙ share

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AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data

Developing high-performing predictive models for large tabular data sets...
10/30/2020 ∙ by Romain Egele, et al. ∙ 0 ∙ share

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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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Prasanna Balaprakash

Featured Co-authors

H. Vincent Poor

Yu Cheng

Nan Li

Isabelle Guyon

Hal Finkel

Ruslan Shaydulin

Lin X. Cai

Rick Stevens

Fangfang Xia

Lukasz Cincio

Yuri Alexeev

Learning-Based Distributed Random Access for Multi-Cell IoT Networks with NOMA

AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data

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

Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks

Graph Neural Network Architecture Search for Molecular Property Prediction

Meta Continual Learning via Dynamic Programming

Multilayer Neuromodulated Architectures for Memory-Constrained Online Continual Learning

A Gradient-Aware Search Algorithm for Constrained Markov Decision Processes

Towards On-Chip Bayesian Neuromorphic Learning

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

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

Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems

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

Value-Added Chemical Discovery Using Reinforcement Learning

Modular Deep Learning Analysis of Galaxy-Scale Strong Lensing Images

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

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

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

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

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

Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning

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