
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...
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Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks
Wide area networking infrastructures (WANs), particularly science and re...
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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...
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Meta Continual Learning via Dynamic Programming
Metacontinual learning algorithms seek to rapidly train a model when fa...
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Multilayer Neuromodulated Architectures for MemoryConstrained Online Continual Learning
We focus on the problem of how to achieve online continual learning unde...
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A GradientAware Search Algorithm for Constrained Markov Decision Processes
The canonical solution methodology for finite constrained Markov decisio...
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Towards OnChip Bayesian Neuromorphic Learning
If edge devices are to be deployed to critical applications where their ...
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Transfer Learning with Graph Neural Networks for ShortTerm Highway Traffic Forecasting
Highway traffic modeling and forecasting approaches are critical for int...
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Constrained Deep Reinforcement Learning for Energy Sustainable MultiUAV based Random Access IoT Networks with NOMA
In this paper, we apply the NonOrthogonal Multiple Access (NOMA) techni...
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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...
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ReinforcementLearningBased Variational Quantum Circuits Optimization for Combinatorial Problems
Quantum computing exploits basic quantum phenomena such as state superpo...
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ValueAdded Chemical Discovery Using Reinforcement Learning
Computerassisted synthesis planning aims to help chemists find better r...
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Modular Deep Learning Analysis of GalaxyScale Strong Lensing Images
Strong gravitational lensing of astrophysical sources by foreground gala...
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GraphPartitioningBased Diffusion Convolution Recurrent Neural Network for LargeScale Traffic Forecasting
Traffic forecasting approaches are critical to developing adaptive strat...
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MaLTESE: LargeScale SimulationDriven Machine Learning for Transient Driving Cycles
Optimal engine operation during a transient driving cycle is the key to ...
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Using recurrent neural networks for nonlinear component computation in advectiondominated reducedorder models
Rapid simulations of advectiondominated problems are vital for multiple...
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Balsam: Automated Scheduling and Execution of Dynamic, DataIntensive HPC Workflows
We introduce the Balsam service to manage highthroughput task schedulin...
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Scalable ReinforcementLearningBased Neural Architecture Search for Cancer Deep Learning Research
Cancer is a complex disease, the understanding and treatment of which ar...
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Neuromorphic Architecture Optimization for TaskSpecific Dynamic Learning
The ability to learn and adapt in real time is a central feature of biol...
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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...
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Prasanna Balaprakash
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