Topic: Deep Learning

The 2021 Conference on Computer Vision and Pattern Recognition, the premier conference of its kind, will feature two papers co-authored by an LLNL researcher targeted at improving the understanding of robust machine learning models.

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New research debuting at ICLR 2021 demonstrates a learning-by-compressing approach to deep learning that outperforms traditional methods without sacrificing accuracy.

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The Data Science Institute sponsored LLNL’s 27th hackathon on February 11–12. Organizers offered a deep learning tutorial and presentations showcasing data science techniques.

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LLNL and IBM research on deep learning models to accurately diagnose diseases from x-ray images won the Best Paper award for Computer-Aided Diagnosis at the SPIE Medical Imaging Conference.

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Three papers address feature importance estimation under distribution shifts, attribute-guided adversarial training, and uncertainty matching in graph neural networks.

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An LLNL team has developed a “Learn-by-Calibrating” method for creating powerful scientific emulators that could be used as proxies for far more computationally intensive simulators.

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Computing’s summer hackathon was held virtually on August 6–7 and featured presentations from teams who tested software technologies, expanded project features, or explored new ways of analyzing data.

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Two papers featuring LLNL scientists were accepted in the 2020 International Conference on Machine Learning (ICML), one of the world’s premier conferences of its kind.

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Surrogate models supported by neural networks could lead to new insights in complicated physics problems such as inertial confinement fusion.

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A team led by an LLNL computer scientist proposes a deep learning approach aimed at improving the reliability of classifier models for predicting disease types from diagnostic images.

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LLNL teams conduct research using AI, and the Machine Learning Reading Group serves as a resource for employees to keep one another apprised of developments in this ever-changing field.

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As part of the Department of Energy’s role in the fight against cancer, scientists are building tools that use supercomputers to solve problems in entirely new ways.

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Brothers and Computation teammates Joe and Sam Eklund discuss their multi-hackathon project using Deep Voice 3.

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LLNL’s Center for Applied Scientific Computing looks back at 2018 papers, presentations, and other activities recognizing research and innovation in data science.

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With nearly 100 publications, CASC researcher Jayaraman “Jay” Thiagarajan explores the possibilities of artificial intelligence and machine learning technologies.

People Highlight

LLNL employees attended a five-part “Deep Learning 101” course, which introduced the basics of neural networks and machine learning to anyone with a basic knowledge of programming in Python.

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