Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Alex Bäuerle"'
Autor:
Alex Bäuerle, Ángel Alexander Cabrera, Fred Hohman, Megan Maher, David Koski, Xavier Suau, Titus Barik, Dominik Moritz
Interfaces for machine learning (ML), information and visualizations about models or data, can help practitioners build robust and responsible ML systems. Despite their benefits, recent studies of ML teams and our interviews with practitioners (n=9)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0eeada118043e5930181f75e4aa3bdf8
http://arxiv.org/abs/2202.08946
http://arxiv.org/abs/2202.08946
Due to the success and growing job market of deep learning (DL), students and researchers from many areas are interested in learning about DL technologies. Visualization has been used as a modern medium during this learning process. However, despite
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f0add9f19f9b6c8b37cffcaf3b1bcd6c
Publikováno v:
AIES
The measurement of bias in machine learning often focuses on model performance across identity subgroups (such as man and woman) with respect to groundtruth labels. However, these methods do not directly measure the associations that a model may have
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::221af3de1a2daa8e357d10674e5f2d9a
http://arxiv.org/abs/2103.03417
http://arxiv.org/abs/2103.03417
Training data plays an essential role in modern applications of machine learning. However, gathering labeled training data is time-consuming. Therefore, labeling is often outsourced to less experienced users, or completely automated. This can introdu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a74acc80117f7a3e7c48c07be5e25503
To convey neural network architectures in publications, appropriate visualizations are of great importance. While most current deep learning papers contain such visualizations, these are usually handcrafted just before publication, which results in a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1529ab712356903f92e1b728b981e0f6
Autor:
Volker Schmidt, Timo Ropinski, Matthias Schmidt, Alex Bäuerle, Matthias Neumann, Marcus Fändrich, Matthias Weber
Summary Detecting crossovers in cryo‐electron microscopy images of protein fibrils is an important step towards determining the morphological composition of a sample. Currently, the crossover locations are picked by hand, which introduces errors an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::41e559cf0b3895edc964b5902977fa9b