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Deep Learning for Science
AI/Machine Learning/Deep Learning
DescriptionThe Deep Learning (DL) for Science workshop provides a forum for practitioners working on any and all aspects of DL for science and engineering in the High Performance Computing (HPC) context to present their latest research results and development, deployment, and application experiences. The general theme of this workshop series is the intersection of DL and HPC; the theme of this particular workshop is the applications of DL methods in and science and engineering: novel uses of DL methods, e.g., convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), and reinforcement learning (RL), in the natural sciences, social sciences, and engineering, to encompass innovative applications of DL in traditional numerical computation. Its scope encompasses application development in scientific scenarios using HPC platforms; DL methods applied to numerical simulation; fundamental algorithms, enhanced procedures, and software development methods to enable scalable training and inference; hardware changes with impact on future supercomputer design; and machine deployment, performance evaluation, and reproducibility practices for DL applications with an emphasis on scientific usage. This workshop will be centered around published papers. Submissions will be peer-reviewed, and accepted papers will be published as part of the Joint Workshop Proceedings by Springer.