[post-doc] Evaluating the propagation of belief functions inside neural networks: a pre-fusion architecture study

This contract is about the research activity of the IRIMAS institute in the field of integration between data fusion and neural networks. The pre-fusion (data fusion before feeding the network) is the center of this post-doctoral contact. The tasks will be :
1 – State of the art of architectures of pre-fusion, cross-fusion and post-fusion, particularly those involving evidential theory and belief functions
2 – Study on the propagation of belief functions and particularly how the ignorance is considered in each layer of different kinds of neural networks (MLP, CNN, Vision Transformers, etc.)
3 – Implementation of the identified pre-fusion scheme within a deep learning architecture
4 – Simulation results and comparison between the different proposed architecture (pre/cross/post fusion) – the hypothesis to be verified is that pre-fusion is a) feasible and b) more performant than cross/post-fusion (regarding evaluation metrics such as F1 score and error rate)
5 – Quantitative comparison of several fusion operators (DS, PCR6/6+, etc.) including performance metrics but also computational cost
(6) – Real-time implementation to the lab prototype may be investigated if desired

More details are given in the attached call for application.

Deadline for application: 01/01/23

Thomas Josso-Laurain (thomas.josso-laurain@uha.fr)