Highlighted Events

Machine Learning

Listed Events
  1. AIops: Bringing Artificial Intelligence to the Data Center (NREL)
  2. Applying Machine Learning to Understand the Write Performance of Large-Scale Parallel Filesystems (ANL, ORNL, SNL)
  3. Benchmarking Machine Learning Ecosystem on HPC Systems (ANL, BNL, LBNL)
  4. BSTC: A Novel Binarized-Soft-Tensor-Core Design for Accelerating Bit-Based Approximated Neural Nets (PNNL)
  5. Channel and Filter Parallelism for Large-Scale CNN Training (LLNL)
  6. DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding (ANL, ORNL)
  7. Deep Kernel Learning for Information Extraction from Cancer Pathology Reports (ORNL)
  8. Deep Learning Accelerated Light Source Experiments (ANL)
  9. Deep Learning at Scale (LBNL)
  10. Deep Learning-Based Feature-Aware Data Modeling for Complex Physics Simulations (LANL)
  11. Deep Learning Enabled Unsupervised State Identification in KRAS Dimers and Interacting Lipids (LANL, LLNL)
  12. Deep Learning on Supercomputers (ANL)
  13. Enabling Machine Learning-Based HPC Performance Diagnostics in Production Environments (LBNL, SNL)
  14. Evolving Larger Convolutional Layer Kernel Sizes for a Settlement Detection Deep-Learner on Summit (ORNL)
  15. Fine-Grained Exploitation of Mixed Precision for Faster CNN Training (ORNL)
  16. Fusion of Structure Based Deep Learning to Accelerate Molecular Docking Predictions (LLNL)
  17. HPC and AI – Accelerating Design of Clean High-Efficiency Engines (ANL)
  18. HPC Big Data and AI: Computing under Constraints (ANL)
  19. Highly-Scalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs (LBNL, PNNL)
  20. Integrating High-Performance Simulations and Learning toward Improved Cancer Therapy (ANL)
  21. Machine Learning Directed Multiscale Simulations To Explore RAS Biology (LLNL)
  22. Machine Learning Guided Optimal Use of GPU Unified Memory (ANL, LLNL)
  23. Machine-Learning Hardware: Architecture, System Interfaces, and Programming Models (ANL)
  24. Machine Learning in HPC Environments (ORNL)
  25. Machine Learning Optimization (LLNL)
  26. Mitigating Communication Bottlenecks in MPI-Based Distributed Learning (BNL)
  27. Neural Networks for the Benchmarking of Detection Algorithms (LBNL)
  28. Opening Remarks: Machine Learning in HPC Environments (ORNL)
  29. PAVE: An In Situ Framework for Scientific Visualization and Machine Learning Coupling (ORNL)
  30. Performance Analysis of Deep Learning Workloads on Leading-Edge Systems (BNL)
  31. Reinforcement Learning for Quantum Approximate Optimization (ANL, LANL)
  32. Scalability and Data Security: Deep Learning with Health Data on Future HPC Platforms (ORNL)
  33. Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research (ANL)
  34. Scientific Machine Learning (PNNL)
  35. Strategies to Deploy and Scale Deep Learning on the Summit Supercomputer (ORNL)
  36. Visualizing Deep Learning at Scale for the Construction of Galaxy Catalogs in the Dark Energy Survey (ANL)

SC Invited Speaker: Nathan Baker of PNNL

Tuesday, Nov 19th – 2:15p.m. to 3:00p.m.: “Scientific Machine Learning” Description: Machine learning (ML) is rapidly changing the field of scientific computing. However, there exist key research gaps that limit the impact of current ML methods on scientific problems. These gaps were identified and discussed at a recent Department of Energy Basic Research Needs Workshop on Scientific Machine Learning. This workshop identified six priority research directions to increase the impact of ML on scientific problems. In this talk, I will review these six areas and present recent results from Pacific Northwest National Laboratory in domain-aware machine learning, one of the six priority research directions.
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