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4,071 to 4,080 of 11,649 Results

gidron_Norway_public.R 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
Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images 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 the need to incorporate multi-scale features arise. The resolution of in...
00_ReadMe.txt Mar 29, 2023 - Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images Plain Text - 6.2 KB - MD5: 66b4fc3733f5c54175c57f7c40d24869
test.zip Mar 29, 2023 - Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images ZIP Archive - 8.7 GB - MD5: 776f8bccbb1285032864afee9cfa991b
train.csv 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
train.zip 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.
Background data for: Pre-hospital identification of infection focus in sepsis and timely empirical antibiotic therapy in a rural ambulance service: A prospective cohort study Aug 20, 2024 Andersson, Lars-Jøran; Simonsen, Gunnar Skov; Solligård, Erik; Fredriksen, Knut, 2024, "Background data for: Pre-hospital identification of infection focus in sepsis and timely empirical antibiotic therapy in a rural ambulance service: A prospective cohort study", https://doi.org/10.18710/VGANVN, DataverseNO, V1 This dataset is extracted from an ambulance quality registry of patients with suspected sepsis managed by the ambulance department of the University hospital of Northern Norway. Data was collected from patients with suspected sepsis who were given pre-hospital intravenous antibiotics and transported to hospital by the University of Northern Norways...
00_README.txt Aug 20, 2024 - Background data for: Pre-hospital identification of infection focus in sepsis and timely empirical antibiotic therapy in a rural ambulance service: A prospective cohort study Plain Text - 7.9 KB - MD5: 554fa9d64efc45d7db2c05e28b6af18d
01_DPIA.pdf Aug 20, 2024 - Background data for: Pre-hospital identification of infection focus in sepsis and timely empirical antibiotic therapy in a rural ambulance service: A prospective cohort study Adobe PDF - 206.6 KB - MD5: e2d431510050165ac1af38c3de4fec04
prehospital_sepsis_antibiotics1.csv Aug 20, 2024 - Background data for: Pre-hospital identification of infection focus in sepsis and timely empirical antibiotic therapy in a rural ambulance service: A prospective cohort study Comma Separated Values - 43.6 KB - MD5: f521d313f0fb09100db22954ea3600ac

gidron_Norway_public.R

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 -

Kode på data fra Gidron

Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images

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 the need to incorporate multi-scale features arise. The resolution of in...

00_ReadMe.txt

Mar 29, 2023 - Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images

Plain Text - 6.2 KB -

test.zip

Mar 29, 2023 - Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images

ZIP Archive - 8.7 GB -

train.csv

Mar 29, 2023 - Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images

Comma Separated Values - 373.1 KB -

For testing purposes you may contact Dilip K. Prasad at dilip.prasad@uit.no

train.zip

Mar 29, 2023 - Supporting Data for: UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images

ZIP Archive - 8.8 GB -

The training files containing all the images of the training set.

Background data for: Pre-hospital identification of infection focus in sepsis and timely empirical antibiotic therapy in a rural ambulance service: A prospective cohort study

Aug 20, 2024

Andersson, Lars-Jøran; Simonsen, Gunnar Skov; Solligård, Erik; Fredriksen, Knut, 2024, "Background data for: Pre-hospital identification of infection focus in sepsis and timely empirical antibiotic therapy in a rural ambulance service: A prospective cohort study", https://doi.org/10.18710/VGANVN, DataverseNO, V1

This dataset is extracted from an ambulance quality registry of patients with suspected sepsis managed by the ambulance department of the University hospital of Northern Norway. Data was collected from patients with suspected sepsis who were given pre-hospital intravenous antibiotics and transported to hospital by the University of Northern Norways...

00_README.txt

Aug 20, 2024 - Background data for: Pre-hospital identification of infection focus in sepsis and timely empirical antibiotic therapy in a rural ambulance service: A prospective cohort study

Plain Text - 7.9 KB -

01_DPIA.pdf

Aug 20, 2024 - Background data for: Pre-hospital identification of infection focus in sepsis and timely empirical antibiotic therapy in a rural ambulance service: A prospective cohort study

Adobe PDF - 206.6 KB -

prehospital_sepsis_antibiotics1.csv

Aug 20, 2024 - Background data for: Pre-hospital identification of infection focus in sepsis and timely empirical antibiotic therapy in a rural ambulance service: A prospective cohort study

Comma Separated Values - 43.6 KB -

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