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ORCID5th MLHPC@SC 2019: Denver, CO, USA
- 2019 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments, MLHPC@SC, Denver, CO, USA, November 18, 2019. IEEE 2019, ISBN 978-1-7281-5985-0
Technical Papers
- Jiali Li
, Bogdan Nicolae, Justin M. Wozniak, George Bosilca:
Understanding Scalability and Fine-Grain Parallelism of Synchronous Data Parallel Training. 1-8 - Jeremy T. Johnston, Steven R. Young, Catherine D. Schuman, Junghoon Chae, Don D. March, Robert M. Patton, Thomas E. Potok:
Fine-Grained Exploitation of Mixed Precision for Faster CNN Training. 9-18 - Greg Heinrich, Iuri Frosio:
Metaoptimization on a Distributed System for Deep Reinforcement Learning. 19-30 - Gabriel Laberge, Shahrzad Shirzad, Patrick Diehl, Hartmut Kaiser, Serge Prudhomme, Adrian S. Lemoine:
Scheduling Optimization of Parallel Linear Algebra Algorithms Using Supervised Learning. 31-43 - Gordon Euhyun Moon, Denis Newman-Griffis, Jinsung Kim, Aravind Sukumaran-Rajam, Eric Fosler-Lussier, P. Sadayappan:
Parallel Data-Local Training for Optimizing Word2Vec Embeddings for Word and Graph Embeddings. 44-55 - Raju Ram, Sabine Müller, Franz-Josef Pfreundt, Nicolas R. Gauger, Janis Keuper:
Scalable Hyperparameter Optimization with Lazy Gaussian Processes. 56-65 - Avraam Chatzimichailidis, Janis Keuper, Franz-Josef Pfreundt, Nicolas R. Gauger:
GradVis: Visualization and Second Order Analysis of Optimization Surfaces during the Training of Deep Neural Networks. 66-74 - Adam Rupe, Prabhat, James P. Crutchfield, Nalini Kumar, Vladislav Epifanov, Karthik Kashinath, Oleksandr Pavlyk, Frank Schlimbach, Mostofa Patwary, Sergey Maidanov, Victor W. Lee:
DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems. 75-87
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