Program at a glance
- Extended program: includes abstracts of all talks.
- General Sessions: final programme. Please, check for any errors and let us know as soon as possible.
- Artificial Intelligence in Nanophotonics Workshop: detailed programme
Information for chairpersons: after your sesion, please fill in this form with your assessment for each presented contribution.
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.
Armand Vilalta Arias
High Performance Artificial Intelligence Group
Departament of Computer Science
Barcelona Supercomputing Center
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.
- One hour theory on fine tunning, transfer leaning and feature extraction.
- One hour practice with the CTE-POWER9 Cluster of the Barcelona Supercomputing Center.
As a part of IWANN 2019 scientific programme, two prizes will be awarded:
– Best contribution.
– Best special session.
The concession will be decided by IWANN 2019 chairs, based upon scientific merit and relevance.
|Artificial Intelligence in Nanophotonics||Dr. Nikolay Zheludev
Dr. Cesare Soci
|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