221 to 230 of 11,076 Results
Nov 21, 2022 - The Stein Rokkan Research Group for Quantitative Social and Political Science
Jonas Stein, 2022, "Appendiks til Blir norsk politikk GAL? Grønn Alternativ Liberal (GAL) – Norske velgere og partier sett gjennom et europeisk perspektiv", https://doi.org/10.18710/F6XSOT, DataverseNO, V1
Online appendix for paper to be published in Norsk statsvitenskapelig tidsskrift |
Nov 21, 2022 -
Appendiks til Blir norsk politikk GAL? Grønn Alternativ Liberal (GAL) – Norske velgere og partier sett gjennom et europeisk perspektiv
Adobe PDF - 117.7 KB -
MD5: 326849cd3c91f97c9c998f7b19f2ad9a
readme-file |
Nov 21, 2022 -
Appendiks til Blir norsk politikk GAL? Grønn Alternativ Liberal (GAL) – Norske velgere og partier sett gjennom et europeisk perspektiv
Adobe PDF - 518.3 KB -
MD5: ad0d81adb9b7d37f36a05fc09e7c0cf1
Online appendiks til artikkel |
Nov 21, 2022 -
Appendiks til Blir norsk politikk GAL? Grønn Alternativ Liberal (GAL) – Norske velgere og partier sett gjennom et europeisk perspektiv
R Syntax - 3.5 KB -
MD5: f8a04119adb47b1c06b08800a2b88c2b
Kode i R for data fra CHES |
Nov 21, 2022 -
Appendiks til Blir norsk politikk GAL? Grønn Alternativ Liberal (GAL) – Norske velgere og partier sett gjennom et europeisk perspektiv
R Syntax - 1.4 KB -
MD5: a88e4eed563f568176caa625f318fb4e
Kode på data fra Gidron |
Mar 29, 2023
Gupta, Deepak K.; Bhamba, Udbhav; Thakur, Abhishek; Gupta, Akash; Sharan, Suraj; Demir, Ertugrul; Prasad, Dilip K., 2023, "Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images", https://doi.org/10.18710/4F4KJS, DataverseNO, V1
Convolutional neural network (CNN) approaches available in the current literature are designed to work primarily with low-resolution images. When applied on very large images, challenges related to GPU memory, smaller receptive field than needed for semantic correspondence and th... |
Mar 29, 2023 -
Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images
Plain Text - 6.2 KB -
MD5: 66b4fc3733f5c54175c57f7c40d24869
|
Mar 29, 2023 -
Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images
ZIP Archive - 8.7 GB -
MD5: 776f8bccbb1285032864afee9cfa991b
|
Mar 29, 2023 -
Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images
Comma Separated Values - 373.1 KB -
MD5: acd730388d00a1102f17a8139106ac42
For testing purposes you may contact Dilip K. Prasad at dilip.prasad@uit.no |
Mar 29, 2023 -
Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images
ZIP Archive - 8.8 GB -
MD5: d6b3f3ca33e41e006e258f93704417e2
The training files containing all the images of the training set. |