Unsupervised Learning with Neural Networks: Autoencoders
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-70388-2_8
Reference12 articles.
1. Kelli D Humbird, J Luc Peterson, and Ryan G McClarren. Parameter inference with deep jointly informed neural networks. Statistical Analysis and Data Mining: The ASA Data Science Journal, 12(6):496–504, 2019.
2. John E Herr, Kevin Koh, Kun Yao, and John Parkhill. Compressing physics with an autoencoder: Creating an atomic species representation to improve machine learning models in the chemical sciences. The Journal of chemical physics, 151(8):084103, 2019.
3. Tana Cardenas, Derek William Schmidt, Evan S Dodd, Theodore Sonne Perry, Deanna Capelli, T Quintana, John A Oertel, Dominic Peterson, Emilio Giraldez, and Robert F Heeter. Design and fabrication of opacity targets for the national ignition facility. Fusion Science and Technology, 73(3):458–466, 2018.
4. Evan S Dodd, Barbara Gloria DeVolder, ME Martin, Natalia Sergeevna Krasheninnikova, Ian Lee Tregillis, Theodore Sonne Perry, RF Heeter, YP Opachich, AS Moore, John L Kline, et al. Hohlraum modeling for opacity experiments on the national ignition facility. Physics of Plasmas, 25(6):063301, 2018.
5. R Paul Drake. High-energy-density physics: foundation of inertial fusion and experimental astrophysics. Springer, 2018.
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