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Software . 2022
License: https://opensource.org/licenses/BSD-2-Clause
Data sources: Datacite
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ZENODO
Software . 2022
License: http://opensource.org/licenses/BSD-2-Clause
Data sources: ZENODO
Claim

DNN Mode Connectivity for TensorFlow

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 27 Sep 2022Publisher:Zenodo
Authors: Fabian Sterman; Constantin von Crailsheim; David Rundel; Amelie Liese;

doi: 10.5281/zenodo.7115620

DNN Mode Connectivity for TensorFlow

Abstract

The content of this repository is based on the PyTorch implementation dnn-mode-connectivity by Timur Garipov and Pavel Izmailov, which is based on their paper Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. This project was part of the Summer 2022 course Applied Deep Learning with TensorFlow and Pytorch at the LMU Munich, supervised by Dr. David Rügamer, Gregor Wiese, Tobias Weber und Daniel Dold.

Keywords

TensorFlow, Mode connectivity, DNN

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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16