TY  - STD
AU  - Mateusz Pyla
AU  - Kamil Deja
AU  - Bartłomiej Twardowski
AU  - Tomasz Trzcinski
PY  - 2023//
TI  - Bayesian Flow Networks in Continual Learning
N2  - Bayesian Flow Networks (BFNs) has been recently proposed as one of the most promising direction to universal generative modelling, having ability to learn any of the data type. Their power comes from the expressiveness of neural networks and Bayesian inference which make them suitable in the context of continual learning. We delve into the mechanics behind BFNs and conduct the experiments to empirically verify the generative capabilities on non-stationary data.
UR  - https://arxiv.org/abs/2310.12001
L1  - http://158.109.8.37/files/PDT2023.pdf
N1  - LAMP
ID  - Mateusz Pyla2023
ER  -