2020
DOI: 10.5281/zenodo.4296287
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nflows: normalizing flows in PyTorch

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Cited by 46 publications
(34 citation statements)
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“…All experiments have been implemented with Python (Python Software Fundation, 2017) using several scientific packages: dMRI signals were simulated with the package dmipy (Fick et al, 2019) and processed using dipy (Garyfallidis et al, 2014) or custom implementations based on numpy . We used the sbi (Tejero-Cantero et al, 2020) and nflows (Durkan et al, 2020) packages for carrying out the LFI procedures and combined them with data structures and functions from pyTorch (Paszke et al, 2019). The figures of results on real experimental data were generated with mayavi (Ramachandran and Varoquaux, 2011).…”
Section: Resultsmentioning
confidence: 99%
“…All experiments have been implemented with Python (Python Software Fundation, 2017) using several scientific packages: dMRI signals were simulated with the package dmipy (Fick et al, 2019) and processed using dipy (Garyfallidis et al, 2014) or custom implementations based on numpy . We used the sbi (Tejero-Cantero et al, 2020) and nflows (Durkan et al, 2020) packages for carrying out the LFI procedures and combined them with data structures and functions from pyTorch (Paszke et al, 2019). The figures of results on real experimental data were generated with mayavi (Ramachandran and Varoquaux, 2011).…”
Section: Resultsmentioning
confidence: 99%
“…For the implementation purpose, we used nflows [28] in the context of normalizing flows and PyTorch-VAE [29] for Variational Autoencoder.…”
Section: Methodsmentioning
confidence: 99%
“…The code for replicating the experiments can be found at https://github.com/SamuelWiqvist/snpla. All experiments were implemented in Python 3.7.4, with normalizing flow models built using the nflows package (Durkan et al, 2020a). The sbi package (Tejero-Cantero et al, 2020) was used to run SMC-ABC, SNPE-C, SNPR-B, and SNL.…”
Section: Computer Environmentmentioning
confidence: 99%
“…2018-05973. We would also like to thank the developers of the following Python packages for providing the software tools used in this paper: sbi (Tejero-Cantero et al, 2020), nflows (Durkan et al, 2020a), PyTorch (Paszke et al, 2019), and POT: Python Optimal Transport (Flamary & Courty, 2017). UP acknowledges support from the Swedish Research Council (Vetenskapsrådet 2019-03924) and the Chalmers AI Research Centre (CHAIR).…”
Section: Acknowledgementsmentioning
confidence: 99%