November 22, 2016

Program

Tutorial on Transfer Learning for Deep Learning

A hands-on tutorial on Transfer Learning for Deep Neural Networks will be carried out during IWANN 2019. The tutorial will include a practical session with connection to the Barcelona Supercomputing Center.

Organizer:

Dario Garcia-Gasulla
High Performance Artificial Intelligence Group
Departament of Computer Science
Barcelona Supercomputing Center

Abstract:

Training deep neural networks from scratch is not easy. Finding a good model for a given problem requires of huge amounts of data, lots of computational power, and a team of DL experts dedicated to the task for weeks. Since we cannot dedicate these resources for every single problem that may be appropriate for deep learning, the community has been actively looking for easier and faster solutions, mostly focused on the reuse of pre-trained deep learning models. This is the main goal of the transfer learning field, which seeks to exploit models designed and trained for a problem A to solve a potentially unrelated problem B. In this tutorial we will introduce the two main approaches to transfer learning, fine tuning and feature extraction, detailing the benefits and handicaps of each one. We will provide hands-on experience on running both types of transfer learning, while working on the CTE-POWER9 cluster hosted at BSC, which includes state-of-the-art Volta GPU racks.

Scheduling:

  • One hour theory on fine tunning, transfer leaning and feature extraction.
  • One hour practice with the CTE-POWER9 Cluster of the Barcelona Supercomputing Center.

Best Paper and Best Special Session Prizes

As a part of IWANN 2019 scientific programme, two prizes will be awarded:
– Best contribution.
– Best special session.

Each award will include a cash prize granted by Springer, publisher of the proceedings within its LNCS series. The support by Springer is gratefully acknowledged.

The concession will be decided by IWANN 2019 chairs, based upon scientific merit and relevance.

Workshop

Artificial Intelligence in Nanophotonics Dr. Nikolay Zheludev
Dr. Cesare Soci

Confirmed Special Sessions

# Title Organizers
SS01 Artificial Neural Network for biomedical image processing

Dr. Yu-Dong Zhang

SS02 Deep learning models in healthcare and biomedicine Dr. Leonardo Franco
Dr. Ruxandra Stoean
Dr. Francisco Veredas
SS03 Deep learning beyond convolution Dr. Miguel Atencia
SS04 Machine Learning in Vision and Robotics Dr. José García-Rodríguez
Dr. Enrique Domínguez
Dr. Ramón Moreno
SS05 Data-driven Intelligent Transportation Systems Dr. Ignacio J. Turías Domínguez
Dr. David Elizondo
Dr. Francisco Ortega Zamorano
SS06 Software Testing and Intelligent Systems Dr. Juan Boubeta
Dr. Pablo C. Cañizares
Dr. Gregorio Díaz
SS07 Deep Learning and Natural Language Processing Dr. Leonor Becerra-Bonache
Dr. M. Dolores Jiménez-López
Dr. Benoit Favre
SS08 Random-Weights Neural Networks Dr. Claudio Gallicchio
SS09 New and future tendencies in Brain-Computer Interface systems Dr. Ricardo Ron
Dr. Ivan Volosyak
SS10 Human Activity Recognition Dr.-Ing. habil. Matthias Pätzold
SS11 Computational Intelligence Methods for Time Series Dr. Héctor Pomares
SS12 Advanced Methods for Personalized/Precision Medicine Dr. Luis Javier Herrera
Dr. Fernando Rojas
SS13 Exploring document information to improve neural summarization models Dr. Luigi Di Caro
SS15 Machine learning in weather observation and forecasting Dr. Juan Luis Navarro-Mesa
Dr. Antonio Ravelo-García
Dr. Carmen Paz Suárez Araujo