| dc.description.abstract | In recent couple of years many automated data analytics models are implemented, they
provide solutions to problems from detection and identification of faces to the translation
between different languages. This tasks are humanly manageable but with a low processing
rate than the complex models. However, the fact that are humanly solvable allows the user
to agree or discard the provided solution. As an example, the translation task could easily
output misleading solutions if the context is not correctly provided and then the user could
improve the input to the model or just discard the translation.
Unfortunately, when this models are applied to large databases is not possible to apply
this double validation and the user might believe blindly in the model output. The above
could lead into a biased decision making, threatening not only the productivity but also the
security of the workers.
Because of this, is neccessary to create models that quantify the uncertainty in the output.
At the following thesis work the possibility to use distributions over single point matrices as
weights is fused with recurrent neural networks (RNNs) a type of neural network which are
specialized dealing with sequential data. The model proposed could be trained as discriminative
probabilistic models thanks to the Bayes theorem and the variational inference.
The proposed model is called Bayesian Variational Recurrent Neural Networks is validated
with the benchmark dataset C-MAPSS which is for remaining useful life (RUL) prognosis.
Also, the model is compared with the same architecture but a frequentist approach
(single points matrices as weights), with different models from the state of the art and finally,
with MC Dropout, another method to quantify the uncertainty in neuronal networks. The
proposed model outperforms every comparison and furthermore, it is tested with two classification
tasks in bearings from University of Ottawa and Politecnico di Torino, and two health
indicator regression tasks, one from a commercial wind turbine from Green Power Monitor
and the last in fatigue crack testing from the University of Maryland showing low error and
good performance in all tasks.
The above proves that the model could be used not only in regression tasks but also in
classification. Finally, it is important to notice that even if the validations are in a mechanical
engineering context, the layers are not limited to them, allowing to be used in another context
with sequential data. | |
