Connectionists: Special Session on Transfer Learning - International Work Conference on Artificial Neural Networks, 10-12 June, 2015

[Jorge M. Santos]

Please consider to contribute to the

 

Special Session on Transfer Learning

International Work Conference on Artificial Neural Networks, 10-12 June,
2015 - http://iwann.ugr.es/2015

 

Transfer Learning (TL) aims to transfer knowledge acquired in one problem,
the source problem, onto another problem, the target problem, dispensing
with the bottom-up construction of the target model. The TL approach has
gained significant interest in the Machine Learning (ML) community since it
paves the way to devise intelligent learning models that can easily be
tailored to many different domains of applicability.

 

The following aspects have recently contributed to the emergence of TL:

• Generalization Theory: TL often produces algorithms with good
generalization capability for different problems;

• Efficient TL algorithms: TL provides learning models that can be applied
with far less computational effort than standard ML methods;

• Unlabeled data: TL can be advantageous since unlabeled data can have
severe implications in some fields of research, such as in the biomedical
field.

 

Some examples of topics for this special session:

• Big Data with Deep Neural Networks;

• Generalization Bounds;

• Domain Adaptation or Covariate Shift;

• Algorithms for TL;

• New advancements in TL;

• Real-world applications.

 

Deadline: 6 February 2015

 

Organizers

Luís M. Silva, Dep. of Mathematics, University of Aveiro, Portugal -
lmas at ua.pt

Jorge M. Santos, Dep. of Mathematics, School of Engineering, Polytechnic of
Porto, Portugal - jms at isep.ipp.pt

 

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