Prasanna Balaprakash

research

∙ 10/08/2022

Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness

Robust machine learning models with accurately calibrated uncertainties ...

Sumegha Premchandar, et al. ∙

research

∙ 10/03/2022

HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization

Distributed data storage services tailored to specific applications have...

Matthieu Dorier, et al. ∙

research

∙ 09/27/2022

Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting

Accurate traffic forecasting is vital to an intelligent transportation s...

Weiheng Zhong, et al. ∙

research

∙ 07/01/2022

Asynchronous Distributed Bayesian Optimization at HPC Scale

Bayesian optimization (BO) is a widely used approach for computationally...

Romain Egele, et al. ∙

research

∙ 06/10/2022

Multifidelity Reinforcement Learning with Control Variates

In many computational science and engineering applications, the output o...

Sami Khairy, et al. ∙

research

∙ 06/01/2022

Sequential Bayesian Neural Subnetwork Ensembles

Deep neural network ensembles that appeal to model diversity have been u...

Sanket Jantre, et al. ∙

research

∙ 04/18/2022

A Taxonomy of Error Sources in HPC I/O Machine Learning Models

I/O efficiency is crucial to productivity in scientific computing, but t...

Mihailo Isakov, et al. ∙

research

∙ 04/04/2022

Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting

Deep-learning-based data-driven forecasting methods have produced impres...

Tanwi Mallick, et al. ∙

research

∙ 03/04/2022

Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck

The information bottleneck framework provides a systematic approach to l...

Anirban Samaddar, et al. ∙

research

∙ 02/22/2022

Multi-fidelity reinforcement learning framework for shape optimization

Deep reinforcement learning (DRL) is a promising outer-loop intelligence...

Sahil Bhola, et al. ∙

research

∙ 12/17/2021

A data-centric weak supervised learning for highway traffic incident detection

Using the data from loop detector sensors for near-real-time detection o...

Yixuan Sun, et al. ∙

research

∙ 11/20/2021

Modeling Design and Control Problems Involving Neural Network Surrogates

We consider nonlinear optimization problems that involve surrogate model...

Dominic Yang, et al. ∙

research

∙ 10/26/2021

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification

Deep neural networks are powerful predictors for a variety of tasks. How...

Romain Egele, et al. ∙

research

∙ 09/28/2021

AutoPhaseNN: Unsupervised Physics-aware Deep Learning of 3D Nanoscale Coherent Imaging

The problem of phase retrieval, or the algorithmic recovery of lost phas...

Yudong Yao, et al. ∙

research

∙ 09/28/2021

Formalizing the Generalization-Forgetting Trade-off in Continual Learning

We formulate the continual learning (CL) problem via dynamic programming...

Krishnan Raghavan, et al. ∙

research

∙ 05/10/2021

Customized Monte Carlo Tree Search for LLVM/Polly's Composable Loop Optimization Transformations

Polly is the LLVM project's polyhedral loop nest optimizer. Recently, us...

Jaehoon Koo, et al. ∙

research

∙ 04/27/2021

Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization (extended version)

In this paper, we develop a ytopt autotuning framework that leverages Ba...

Xingfu Wu, et al. ∙

research

∙ 02/16/2021

Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics

In this paper we consider the problem of learning a regression function ...

Jongeun Kim, et al. ∙

research

∙ 01/02/2021

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

Non-orthogonal multiple access (NOMA) is a key technology to enable mass...

Sami Khairy, et al. ∙

research

∙ 10/30/2020

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...

Romain Egele, et al. ∙

research

∙ 10/15/2020

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...

Xingfu Wu, et al. ∙

research

∙ 08/28/2020

Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks

Wide area networking infrastructures (WANs), particularly science and re...

Tanwi Mallick, et al. ∙

research

∙ 08/27/2020

Graph Neural Network Architecture Search for Molecular Property Prediction

Predicting the properties of a molecule from its structure is a challeng...

Shengli Jiang, et al. ∙

research

∙ 08/05/2020

Meta Continual Learning via Dynamic Programming

Meta-continual learning algorithms seek to rapidly train a model when fa...

R. Krishnan, et al. ∙

research

∙ 07/16/2020

Multilayer Neuromodulated Architectures for Memory-Constrained Online Continual Learning

We focus on the problem of how to achieve online continual learning unde...

Sandeep Madireddy, et al. ∙

research

∙ 05/07/2020

A Gradient-Aware Search Algorithm for Constrained Markov Decision Processes

The canonical solution methodology for finite constrained Markov decisio...

Sami Khairy, et al. ∙

research

∙ 05/05/2020

Towards On-Chip Bayesian Neuromorphic Learning

If edge devices are to be deployed to critical applications where their ...

Nathan Wycoff, et al. ∙

research

∙ 04/17/2020

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

Highway traffic modeling and forecasting approaches are critical for int...

Tanwi Mallick, et al. ∙

research

∙ 01/31/2020

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...

Sami Khairy, et al. ∙

research

∙ 11/25/2019

Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems

Quantum computing is a computational paradigm with the potential to outp...

Sami Khairy, et al. ∙

research

∙ 11/11/2019

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

Quantum computing exploits basic quantum phenomena such as state superpo...

Sami Khairy, et al. ∙

research

∙ 11/10/2019

Value-Added Chemical Discovery Using Reinforcement Learning

Computer-assisted synthesis planning aims to help chemists find better r...

Peihong Jiang, et al. ∙

research

∙ 11/10/2019

Modular Deep Learning Analysis of Galaxy-Scale Strong Lensing Images

Strong gravitational lensing of astrophysical sources by foreground gala...

Sandeep Madireddy, et al. ∙

research

∙ 09/24/2019

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

Traffic forecasting approaches are critical to developing adaptive strat...

Tanwi Mallick, et al. ∙

research

∙ 09/22/2019

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

Optimal engine operation during a transient driving cycle is the key to ...

Shashi M. Aithal, et al. ∙

research

∙ 09/18/2019

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

Rapid simulations of advection-dominated problems are vital for multiple...

Romit Maulik, et al. ∙

research

∙ 09/18/2019

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

We introduce the Balsam service to manage high-throughput task schedulin...

Michael A. Salim, et al. ∙

research

∙ 09/01/2019

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

Cancer is a complex disease, the understanding and treatment of which ar...

Prasanna Balaprakash, et al. ∙

research

∙ 06/04/2019

Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning

The ability to learn and adapt in real time is a central feature of biol...

Sandeep Madireddy, et al. ∙

research

∙ 04/29/2019

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

As neural networks have begun performing increasingly critical tasks for...

Nathan Wycoff, et al. ∙

Prasanna Balaprakash

Featured Co-authors