303 citations · 960 across the 44 of their papers we have counts for
13 papers · 1 filter
Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems
Sami Khairy, Ruslan Shaydulin, Lukasz Cincio +2
Quantum computing is a computational paradigm with the potential to outperform classical methods for a variety of problems. Proposed recently, the Quantum Approximate Optimization…
Reinforcement-Learning-Based Variational Quantum Circuits Optimization for Combinatorial Problems
Sami Khairy, Ruslan Shaydulin, Lukasz Cincio +2
Quantum computing exploits basic quantum phenomena such as state superposition and entanglement to perform computations. The Quantum Approximate Optimization Algorithm (QAOA) is ar…
Value-Added Chemical Discovery Using Reinforcement Learning
Peihong Jiang, Hieu Doan, Sandeep Madireddy +2
Computer-assisted synthesis planning aims to help chemists find better reaction pathways faster. Finding viable and short pathways from sugar molecules to value-added chemicals can…
A Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and Modeling
Sandeep Madireddy, Nesar Ramachandra, Nan Li +5
Upcoming large astronomical surveys are expected to capture an unprecedented number of strong gravitational lensing systems. Deep learning is emerging as a promising practical tool…
Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models
Romit Maulik, Vishwas Rao, Sandeep Madireddy +2
Rapid simulations of advection-dominated problems are vital for multiple engineering and geophysical applications. In this paper, we present a long short-term memory neural network…
MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles
Shashi M. Aithal, Prasanna Balaprakash
Optimal engine operation during a transient driving cycle is the key to achieving greater fuel economy, engine efficiency, and reduced emissions. In order to achieve continuously o…
Balsam: Automated Scheduling and Execution of Dynamic, Data-Intensive HPC Workflows
Michael A. Salim, Thomas D. Uram, J. Taylor Childers +3
We introduce the Balsam service to manage high-throughput task scheduling and execution on supercomputing systems. Balsam allows users to populate a task database with a variety of…
Graph-Partitioning-Based Diffusion Convolutional Recurrent Neural Network for Large-Scale Traffic Forecasting
Tanwi Mallick, Prasanna Balaprakash, Eric Rask +1
Traffic forecasting approaches are critical to developing adaptive strategies for mobility. Traffic patterns have complex spatial and temporal dependencies that make accurate forec…
Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research
Prasanna Balaprakash, Romain Egele, Misha Salim +5
Cancer is a complex disease, the understanding and treatment of which are being aided through increases in the volume of collected data and in the scale of deployed computing power…
Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning
Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash
The ability to learn and adapt in real time is a central feature of biological systems. Neuromorphic architectures demonstrating such versatility can greatly enhance our ability to…
Time-series learning of latent-space dynamics for reduced-order model closure
Romit Maulik, Arvind Mohan, Bethany Lusch +3
We study the performance of long short-term memory networks (LSTMs) and neural ordinary differential equations (NODEs) in learning latent-space representations of dynamical equatio…
Neuromorphic Acceleration for Approximate Bayesian Inference on Neural Networks via Permanent Dropout
Nathan Wycoff, Prasanna Balaprakash, Fangfang Xia
As neural networks have begun performing increasingly critical tasks for society, ranging from driving cars to identifying candidates for drug development, the value of their abili…
Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection
Sandeep Madireddy, Ding-Wen Chung, Troy Loeffler +4
Atom-probe tomography (APT) facilitates nano- and atomic-scale characterization and analysis of microstructural features. Specifically, APT is well suited to study the interfacial…