We are developing a deep-learning neural network system to discover and assess patterns in multimodal data gathered worldwide across all media fields for indicators of nuclear proliferation capabilities and activities, revolutionizing our data analytic capabilities to support national security, nonproliferation, cyber and space security, intelligence, and biosecurity.
Publications and Presentations
Chen, B. 2018. "Deep Learning: A Guide for Practitioners in the Physical Sciences." Phys. Plasmas. 25. doi: 10.1063/1.5020791. LLNL-JRNL-743970.
Cong, G. et al. 2018. "Accelerating Deep Neural Network Training for Action Recognition on a Cluster of GPUs." Supercomputing, Dallas, TX, Nov. 2018. LLNL-CONF-760618.
Feldman, Y., et al. 2018. "Toward a Multimodal-Deep Learning Retrieval System for Monitoring Nuclear Proliferation Activities." Journal of Nuclear Materials Management. LLNL-JRNL-751737.
Mundhenk, T. N., D. Ho, and B. Chen. 2018. "Improvements to Context Based Self-Supervised Learning." CVPR, Salt Lake City, UT, June 2018. LLNL-CONF-741553.