Mark Coletti Staff Scientist Contact 865.241.5148 | COLETTIMA@ORNL.GOV All Publications Avoiding excess computation in asynchronous evolutionary algorithms Avoiding Excess Computation in Asynchronous Evolutionary Algorithms... Troubleshooting deep-learner training data problems using an evolutionary algorithm on Summit Validating Safecast data by comparisons to a Department of Energy Fukushima Prefecture aerial survey... Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications Workflow Provenance in the Computing Continuum for Responsible, Trustworthy, and Energy-Efficient AI Efficacy of using a dynamic length representation vs. a fixed-length for neuroarchitecture search Towards Cross-Facility Workflows Orchestration through Distributed Automation... Assessing Impacts of Atmospheric Conditions on Efficiency and Siting of Large-Scale Direct Air Capture Facilities A dataset of recorded electricity outages by United States county 2014–2022 SuperNeuro: A Fast and Scalable Simulator for Neuromorphic Computing Predicted structural proteome of Sphagnum divinum and proteome-scale annotation Multiobjective Hyperparameter Optimization for Deep Learning Interatomic Potential Training Using NSGA-II Research Software Engineering at 91°µÍø... Neuromorphic Computing for Scientific Applications Toward designing effective exascale scientific computing workflows: experiences and best practices Training reinforcement learning models via an adversarial evolutionary algorithm Proteome-scale Deployment of Protein Structure Prediction Workflows on the Summit Supercomputer Smoky Mountain Data Challenge 2021: An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing Diagnosing autonomous vehicle driving criteria with an adversarial evolutionary algorithm Quantitative Evaluation of Autonomous Driving in CARLA Multi-Objective Hyperparameter Optimization for Spiking Neural Network Neuroevolution... Global Partitioning Elevation Normalization Applied to Building Footprint Prediction Library for Evolutionary Algorithms in Python (LEAP) Evolving Larger Convolutional Layer Kernel Sizes for a Settlement Detection Deep-Learner on Summit Pagination Current page 1 Page 2 Next page ›â¶Äº Last page Last » Key Links Curriculum Vitae Organizations Computing and Computational Sciences Directorate Computer Science and Mathematics Division Data and AI Systems Section Learning Systems Group
