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Variational Learning is Effective for Large Deep Networks (bibtex)
Variational Learning is Effective for Large Deep Networks (bibtex)
by Y Shen, N Daheim, B Cong, P Nickl, GM Marconi, C Bazan, R Yokota, I Gurevych, D Cremers, ME Khan and T Möllenhoff
Reference:
Variational Learning is Effective for Large Deep Networks (Y Shen, N Daheim, B Cong, P Nickl, GM Marconi, C Bazan, R Yokota, I Gurevych, D Cremers, ME Khan and T Möllenhoff), In International Conference on Machine Learning (ICML), 2024. ([code][blog][tutorial])
Bibtex Entry:
@inproceedings{shen2024variational,
 title = {Variational Learning is Effective for Large Deep Networks},
 author = {Y Shen and N Daheim and B Cong and P Nickl and GM Marconi and C Bazan and R Yokota and I Gurevych and D Cremers and ME Khan and T Möllenhoff},
 booktitle = {International Conference on Machine Learning (ICML)},
 year = {2024},
 award = {Spotlight},
 eprint = {2402.17641},
 eprinttype = {arXiv},
 eprintclass = {cs.LG},
 keywords = {deep learning},
}
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Informatik IX
Chair of Computer Vision & Artificial Intelligence

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

Follow us on:
CVG Group DVL Group SRL Group