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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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