127 citations · 145 across the 10 of their papers we have counts for
10 papers
AutoML for neuromorphic computing and application-driven co-design: asynchronous, massively parallel optimization of spiking architectures
Angel Yanguas-Gil, Sandeep Madireddy
In this work we have extended AutoML inspired approaches to the exploration and optimization of neuromorphic architectures. Through the integration of a parallel asynchronous model…
General policy mapping: online continual reinforcement learning inspired on the insect brain
Angel Yanguas-Gil, Sandeep Madireddy
We have developed a model for online continual or lifelong reinforcement learning (RL) inspired on the insect brain. Our model leverages the offline training of a feature extractio…
Single Gaussian Process Method for Arbitrary Tokamak Regimes with a Statistical Analysis
Jarrod Leddy, Sandeep Madireddy, Eric Howell +1
Gaussian Process Regression (GPR) is a Bayesian method for inferring profiles based on input data. The technique is increasing in popularity in the fusion community due to its many…
Neuromodulated Neural Architectures with Local Error Signals for Memory-Constrained Online Continual Learning
Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash
The ability to learn continuously from an incoming data stream without catastrophic forgetting is critical for designing intelligent systems. Many existing approaches to continual…
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…
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…
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…