Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Geoff Pleiss"'
Autor:
Matthew Krogstad, David Mandrus, Puspa Upreti, Raymond Osborn, Stephan Rosenkranz, Daniel Phelan, Lekh Poudel, Michael R. Norman, Jacob Ruff, Krishnanand Mallayya, Geoff Pleiss, Kilian Q. Weinberger, Michael Matty, Eun-Ah Kim, Varsha Kishore, Jordan Venderley, Andrew Gordon Wilson
Publikováno v:
Proceedings of the National Academy of Sciences. 119
The information content of crystalline materials becomes astronomical when collective electronic behavior and their fluctuations are taken into account. In the past decade, improvements in source brightness and detector technology at modern X-ray fac
Autor:
James Knighton, Geoff Pleiss, Scott Steinschneider, Steven Lyon, M. Todd Walter, Elizabeth K Carter
Publikováno v:
Journal of Hydrometeorology. 20:883-900
Current generation general circulation models (GCMs) simulate synoptic-scale climate state variables such as geopotential heights, specific humidity, and integrated vapor transport (IVT) more reliably than mesoscale precipitation. Statistical downsca
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff96793c466943f4f5667a7f15401540
http://arxiv.org/abs/2001.02394
http://arxiv.org/abs/2001.02394
Autor:
Kilian Q. Weinberger, Kavita Bala, Paul Upchurch, Robert Pless, Noah Snavely, Jacob R. Gardner, Geoff Pleiss
Publikováno v:
CVPR
We propose Deep Feature Interpolation (DFI), a new data-driven baseline for automatic high-resolution image transformation. As the name suggests, it relies only on simple linear interpolation of deep convolutional features from pre-trained convnets.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4f29fb754eb16a9b07d626b938860104