## 8 Conclusions
(2013) [PERSON]; [PERSON]; [PERSON]; [PERSON] Global analytic solution of fully-observed variational Bayesian matrix factorization. _[PERSON]. Learn. Res._**2013**, _14_, 1-37. * [PERSON] (2023) [PERSON]; [PERSON] VBMF, Version 1.0.0. Available online: [[https://github.com/[PERSON]/VBMF](https://github.com/CasvandenBogaard/VBMF)]([https://github.com/CasvandenBogaard/VBMF](https://github.com/CasvandenBogaard/VBMF)) (accessed on 30 January 2023). * [PERSON] et al. (2010) [PERSON]; [PERSON] [PERSON]; [PERSON] Global analytic solution for variational Bayesian matrix factorization. _Adv. Neural Inf. Process. Syst._**2010**, _23_, 1768-1776. * [PERSON] et al. (2015) [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] Binaryconnect: Training deep neural networks with binary weights during propagations. _arXiv_**2015**, arXiv:1511.00363. * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Bi-real net: Binarizing deep network towards real-network performance. _Int. J. Comput. Vis._**2020**, _128_, 202-219. [CrossRef]
* [PERSON] et al. (2013) [PERSON] [PERSON]; [PERSON]; [PERSON] Estimating or propagating gradients through stochastic neurons for conditional computation. _arXiv_**2013**, arXiv:1308.3432. * [PERSON] et al. (1998) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Gradient-based learning applied to document recognition. _Proc. IEEE_**1998**, _86_, 2278-2324. [CrossRef]
* [PERSON] et al. (2016) [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; et al. Pytorch: An imperative style, high-performance deep learning library. _arXiv_**2019**, arXiv:1912.01703. * [PERSON] et al. (2016) [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON] Tensorly: Tensor learning in python. _arXiv_**2016**, arXiv:1610.09555. * [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON] Network in network. _arXiv_**2013**, arXiv:1312.4400. * [PERSON] et al. (2009) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Imagenet: A large-scale hierarchical image database. In Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 20-25 June 2009; pp. 248-255. * [PERSON] et al. (2018) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON] Dynamical isometry and a mean field theory of CNNs: How to train 10,000-layer vanilla convolutional neural networks. In Proceedings of the International Conference on Machine Learning, Stockholm, Sweden, 10-15 July 2018; pp. 5393-5402. * [PERSON] et al. (2019) [PERSON], [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] A mean field theory of batch normalization. _arXiv_**2019**, arXiv:1902.08129. * [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Deep information propagation. _arXiv_**2016**, arXiv:1611.01232. * [PERSON] and [PERSON] (2021) [PERSON] [PERSON]; [PERSON] Mean field analysis of deep neural networks. _Math. Oper. Res._**2021**, _47_, 120-152. [CrossRef]
* [PERSON] and [PERSON] (2017) [PERSON], [PERSON]; [PERSON] [PERSON] Mean field residual networks: On the edge of chaos. _arXiv_**2017**, arXiv:1712.08969. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON] Mean-field analysis of batch normalization. _arXiv_**2019**, arXiv:1903.02606. * [PERSON] (2018) [PERSON] Deep learning using rectified linear units (relu). _arXiv_**2018**, arXiv:1803.08375. * [PERSON] (2019) [PERSON] Mish: A self regularized non-monotonic activation function. _arXiv_**2019**, arXiv:1908.08681. * [PERSON] et al. (2017) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Mobilenets: Efficient convolutional neural networks for mobile vision applications. _arXiv_**2017**, arXiv:1704.04861. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] Mobilenetv2: Inverted residuals and linear bottlenecks.