CAEPIA
I Workshop in Deep Learning
In recent years, several types of deep neural networks, commonly called Deep Learning (DL) models, have shown a very high potential in the recognition of both spatial and temporal patterns in different types of data. A good example of these networks is the convolutional neural networks (CNNs), which currently constitute the-state-of-the art in the field of computer vision in tasks such as, classification of images, detection of objects in images and videos, or segmentation of images. In fact, since 2012, all the categories of the prestigious Large Scale Visual Recognition Challenge (ILSVRC) have been exclusively won by CNNs. Another good example is the Long Short-Term Memory (LSTM) networks, which currently represent the state-of-the art in Natural Language Processing (NLP) in tasks such as, word disambiguation or opinion analysis. DL networks are increasingly explored in new areas and data.
With DEEPL workshop we wish to encourage the participation of researchers who work in the development and / or application of DL models on different types of data and applications. It will be an opportunity for the presentation of results and the exchange of ideas on this area.
Chairs
- Siham Tabik, University of Granada, Email: siham@ugr.es
- Juan Manuel Górriz, University of Granada, Email: gorriz@ugr.es
- Antonio Bahamonde, University of Oviedo, Email: abahamonde@uniovi.es
Granada
Jamás se le ha oido a nadie decir una palabra que no sea bella para referirse a la Alhambra - James A. Michener
Topics
Topics of interest for this Workshop include (but are not limited to) the following:
- Deep learning algorithms and models, e.g., convolutional neural networks, recurrent neural network, etc.
- Unsupervised feature learning models, e.g., restricted Boltzmann machines, autoencoders, sparse coding, etc.
- Semi-supervised deep learning
- Transfer learning and data-augmentation
- Optimization of Deep Learning models during the inference/deployment phase
- Application of deep learning to diverse types of data, e.g., images, videos, natural language, etc.
Program Committee
| Fernando de la Calle | Carlos III University of Madrid |
| José Dorronsoro | Autonomous University of Madrid |
| Lucas García | MathWorks |
| Pablo Mesejo | University of Granada |
| Ricardo Guerrero Gómez-Olmedo | BEEVA |
| Francisco Javier Ordóñez | StyleSage |
| Andrés Ortiz | University of Malaga |
| Gema Parreño Piqueras | BBVA |
| Petia Radeva | University of Barcelona |
| Javier Ramírez | University of Granada |
| Xavier Serra | University of Pompeu Fabra |