A kutatási téma leírása:
Artificial Neural Networks gains popularity with the widespread of Deep
Due to the ever growing computational power, bigger and more complex
artificial neural networks can be trained to solve more difficult problems.
Artificial Neural Networks are applied in a wide range of disciplines
such as economy, finance, engineering, healthcare, agriculture or IT
The most common tasks are regression, modeling, classification and
Development of more and more intelligent software and applications just
increases the need for these systems.
So the researchers are focusing on the training bigger artificial neural
networks which can solve more complex tasks and software engineers are
seeking the market gaps and application areas.
Even the pruning of artificial networks is popular topic these days
because it allows their usage in embedded systems where the
computational capacity is limited.
Although training, application and even pruning of artificial neural
networks are actively researched and popular topics their inversion is
also a vital and important topic.
Inversion of artificial neural networks can be considered as a parameter
searching problem where the model is defined by the neural network.
The input parameters are searched which yields the expected output.
Inversion can be applied for any regression and modeling task where
feed-forward artificial neural networks are applied.
For example, in a production process the configuration of the machines
affects on the product.
So inversion of artificial neural network which models the production
process allows us to find some settings which results products with
Finding a single input or a set of possible solutions is also a task of
This research project has three major goals. Firstly, it gives an
overview and analyzes the applicability of existing inverting techniques
in the field of deep learning.
Secondly, the project investigate the applicability and viability of
inverted artificial networks in modern web applications and distributed
Finally, the theoretical results should be demonstrated via examples.
előírt nyelvtudás: angol
felvehető hallgatók száma: 1
Jelentkezési határidő: 2019-10-01