Variational Neural Networks implementation in Pytorch and JAX

Illia Oleksiienko; Dat Thanh Tran; Alexandros Iosifidis

doi:10.1016/j.simpa.2022.100431

Variational Neural Networks implementation in Pytorch and JAX

Illia Oleksiienko^*
, Dat Thanh Tran
, Alexandros Iosifidis

^*Tämän työn vastaava kirjoittaja

Tietotekniikka

Tutkimustuotos: Artikkeli › Tieteellinen › vertaisarvioitu

3 Sitaatiot (Scopus)

17 Lataukset (Pure)

Abstrakti

Bayesian Neural Networks consider a distribution over the network's weights, which provides a tool to estimate the uncertainty of a neural network by sampling different models for each input. Variational Neural Networks (VNNs) consider a probability distribution over each layer's outputs and generate parameters for it with the corresponding sub-layers. We provide two Python implementations of VNNs with PyTorch and JAX machine learning libraries that ensure reproducibility of the experimental results and allow implementing uncertainty estimation methods easily in other projects.

Alkuperäiskieli	Englanti
Artikkeli	100431
Julkaisu	Software Impacts
Vuosikerta	14
DOI - pysyväislinkit	https://doi.org/10.1016/j.simpa.2022.100431
Tila	Julkaistu - marrask. 2022
OKM-julkaisutyyppi	A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Rahoitus

This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871449 (OpenDR). This publication reflects the authors’ views only. The European Commission is not responsible for any use that may be made of the information it contains. This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 871449 (OpenDR). This publication reflects the authors’ views only. The European Commission is not responsible for any use that may be made of the information it contains.

Julkaisufoorumi-taso

Jufo-taso 0

!!ASJC Scopus subject areas

Software

Pääsy asiakirjaan

10.1016/j.simpa.2022.100431Lisenssi: CC BY

1-s2.0-S2665963822001154-mainFinal published version, 386 KBLisenssi: CC BY

https://urn.fi/URN:NBN:fi:tuni-202212129074Lisenssi: CC BY

Siteeraa tätä

@article{c8ee14865f3c48549fc57cb9841a223e,

title = "Variational Neural Networks implementation in Pytorch and JAX",

abstract = "Bayesian Neural Networks consider a distribution over the network's weights, which provides a tool to estimate the uncertainty of a neural network by sampling different models for each input. Variational Neural Networks (VNNs) consider a probability distribution over each layer's outputs and generate parameters for it with the corresponding sub-layers. We provide two Python implementations of VNNs with PyTorch and JAX machine learning libraries that ensure reproducibility of the experimental results and allow implementing uncertainty estimation methods easily in other projects.",

keywords = "Bayesian deep learning, Bayesian Neural Networks, JAX, PyTorch, Uncertainty estimation",

author = "Illia Oleksiienko and Tran, \{Dat Thanh\} and Alexandros Iosifidis",

note = "Funding Information: This work has received funding from the European Union{\textquoteright}s Horizon 2020 research and innovation programme under grant agreement No 871449 (OpenDR). This publication reflects the authors{\textquoteright} views only. The European Commission is not responsible for any use that may be made of the information it contains. Funding Information: This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 871449 (OpenDR). This publication reflects the authors{\textquoteright} views only. The European Commission is not responsible for any use that may be made of the information it contains. Publisher Copyright: {\textcopyright} 2022 The Author(s)",

year = "2022",

month = nov,

doi = "10.1016/j.simpa.2022.100431",

language = "English",

volume = "14",

journal = "Software Impacts",

issn = "2665-9638",

publisher = "Elsevier",

}

TY - JOUR

T1 - Variational Neural Networks implementation in Pytorch and JAX

AU - Oleksiienko, Illia

AU - Tran, Dat Thanh

AU - Iosifidis, Alexandros

N1 - Funding Information: This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871449 (OpenDR). This publication reflects the authors’ views only. The European Commission is not responsible for any use that may be made of the information it contains. Funding Information: This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 871449 (OpenDR). This publication reflects the authors’ views only. The European Commission is not responsible for any use that may be made of the information it contains. Publisher Copyright: © 2022 The Author(s)

PY - 2022/11

Y1 - 2022/11

N2 - Bayesian Neural Networks consider a distribution over the network's weights, which provides a tool to estimate the uncertainty of a neural network by sampling different models for each input. Variational Neural Networks (VNNs) consider a probability distribution over each layer's outputs and generate parameters for it with the corresponding sub-layers. We provide two Python implementations of VNNs with PyTorch and JAX machine learning libraries that ensure reproducibility of the experimental results and allow implementing uncertainty estimation methods easily in other projects.

AB - Bayesian Neural Networks consider a distribution over the network's weights, which provides a tool to estimate the uncertainty of a neural network by sampling different models for each input. Variational Neural Networks (VNNs) consider a probability distribution over each layer's outputs and generate parameters for it with the corresponding sub-layers. We provide two Python implementations of VNNs with PyTorch and JAX machine learning libraries that ensure reproducibility of the experimental results and allow implementing uncertainty estimation methods easily in other projects.

KW - Bayesian deep learning

KW - Bayesian Neural Networks

KW - JAX

KW - PyTorch

KW - Uncertainty estimation

U2 - 10.1016/j.simpa.2022.100431

DO - 10.1016/j.simpa.2022.100431

M3 - Article

AN - SCOPUS:85140775113

SN - 2665-9638

VL - 14

JO - Software Impacts

JF - Software Impacts

M1 - 100431

ER -

Variational Neural Networks implementation in Pytorch and JAX

Abstrakti

Rahoitus

Julkaisufoorumi-taso

!!ASJC Scopus subject areas

Pääsy asiakirjaan

Sormenjälki

Siteeraa tätä