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09/12/2023
Comparing Llama-2 and GPT-3 LLMs for HPC kernels generation
We evaluate the use of the open-source Llama-2 model for generating well...
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08/08/2023
Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures
The ability to learn continuously from an incoming data stream without c...
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07/28/2023
Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization?
Hyperparameter optimization (HPO) is crucial for fine-tuning machine lea...
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07/19/2023
Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search
Graph Neural Networks (GNNs) have emerged as a prominent class of data-d...
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06/27/2023
Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation
We evaluate AI-assisted generative capabilities on fundamental numerical...
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06/16/2023
Flow-Bench: A Dataset for Computational Workflow Anomaly Detection
A computational workflow, also known as workflow, consists of tasks that...
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05/19/2023
Learning Continually on a Sequence of Graphs – The Dynamical System Way
Continual learning (CL) is a field concerned with learning a series of i...
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03/28/2023
ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales
As we enter the exascale computing era, efficiently utilizing power and ...
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03/15/2023
Application of probabilistic modeling and automated machine learning framework for high-dimensional stress field
Modern computational methods, involving highly sophisticated mathematica...
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02/20/2023
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles
Classical problems in computational physics such as data-driven forecast...
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02/03/2023
Analyzing the impact of climate change on critical infrastructure from the scientific literature: A weakly supervised NLP approach
Natural language processing (NLP) is a promising approach for analyzing ...
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10/08/2022
Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness
Robust machine learning models with accurately calibrated uncertainties ...
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10/03/2022
HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization
Distributed data storage services tailored to specific applications have...
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09/27/2022
Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting
Accurate traffic forecasting is vital to an intelligent transportation s...
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07/01/2022
Asynchronous Distributed Bayesian Optimization at HPC Scale
Bayesian optimization (BO) is a widely used approach for computationally...
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06/10/2022
Multifidelity Reinforcement Learning with Control Variates
In many computational science and engineering applications, the output o...
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06/01/2022
Sequential Bayesian Neural Subnetwork Ensembles
Deep neural network ensembles that appeal to model diversity have been u...
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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...
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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...
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03/04/2022
Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck
The information bottleneck framework provides a systematic approach to l...
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02/22/2022
Multi-fidelity reinforcement learning framework for shape optimization
Deep reinforcement learning (DRL) is a promising outer-loop intelligence...
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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...
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11/20/2021
Modeling Design and Control Problems Involving Neural Network Surrogates
We consider nonlinear optimization problems that involve surrogate model...
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10/26/2021
AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification
Deep neural networks are powerful predictors for a variety of tasks. How...
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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...
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09/28/2021
Formalizing the Generalization-Forgetting Trade-off in Continual Learning
We formulate the continual learning (CL) problem via dynamic programming...
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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...
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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...
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02/16/2021
Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics
In this paper we consider the problem of learning a regression function ...
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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...
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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...
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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...
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08/28/2020
Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks
Wide area networking infrastructures (WANs), particularly science and re...
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08/27/2020
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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08/05/2020
Meta Continual Learning via Dynamic Programming
Meta-continual learning algorithms seek to rapidly train a model when fa...
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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...
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05/07/2020
A Gradient-Aware Search Algorithm for Constrained Markov Decision Processes
The canonical solution methodology for finite constrained Markov decisio...
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05/05/2020
Towards On-Chip Bayesian Neuromorphic Learning
If edge devices are to be deployed to critical applications where their ...
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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...
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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...
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11/25/2019
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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11/11/2019
Reinforcement-Learning-Based Variational Quantum Circuits Optimization for Combinatorial Problems
Quantum computing exploits basic quantum phenomena such as state superpo...
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11/10/2019
Value-Added Chemical Discovery Using Reinforcement Learning
Computer-assisted synthesis planning aims to help chemists find better r...
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11/10/2019
Modular Deep Learning Analysis of Galaxy-Scale Strong Lensing Images
Strong gravitational lensing of astrophysical sources by foreground gala...
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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...
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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 ...
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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...
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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...
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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...
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06/04/2019
