Highlights include power grid resilience, asynchronous communication in HPC workloads, trustworthy AI, and LLM-boosted compiler optimization.
Topic: Computer Vision
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AI/ML | Algorithms at Scale | Collaborations | Compiler Technology | Computational Math | Computational Science | Computer Vision | Critical Infrastructure | Cyber Incident Response | Cyber Research | Cyber Security | Data Movement and Memory | Data Science | Deep Learning | HPC Systems and Software | ML Theory | Natural Language Processing | Open-Source Software | Performance, Portability, and Productivity | Programming Languages and Models | UQ and Statistics
New CASC research puts sparse autoencoders and concept bottlenecks to work on foundation models.
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LLNL researchers have posters and workshop papers accepted to the IEEE/CVF Conference on Computer Vision and Pattern Recognition on June 3–7.
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New research reveals subtleties in the performance of neural image compression methods, offering insights toward improving these models for real-world applications.
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Highlights include MFEM community workshops, compiler co-design, HPC standards committees, and AI/ML for national security.
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AI/ML | Biology/Biomedicine | Co-Design | Collaborations | Compiler Technology | Computational Math | Computational Science | Computer Vision | Data Science | Deep Learning | Discrete Mathematics | Exascale | HPC Systems and Software | Natural Language Processing | Open-Source Software | Performance, Portability, and Productivity | Programming Languages and Models