International Summer School on Deep Learning
Bilbao, Spain, July 17-21, 2017
DeustoTech, University of Deusto
Rovira i Virgili University
We thank Wikimedia commons for the photos.
DeepLearn 2017 will be a research training event with a global scope aiming at updating
participants about the most recent advances in the critical and fast developing area of
deep learning. This is a branch of artificial intelligence covering a spectrum of current
exciting machine learning research and industrial innovation that provides more efficient
algorithms to deal with large-scale data in neuroscience, computer vision, speech
recognition, language processing, drug discovery, biomedical informatics, recommender
systems, learning theory, robotics, games, etc. Renowned academics and industry pioneers will lecture
and share their views with the audience.
Most deep learning subareas will be displayed, and main challenges identified through 4
keynote lectures, 31 six-hour courses, and 1 round table, which will tackle the most active
and promising topics. The organizers are convinced that outstanding speakers will attract
the brightest and most motivated students. Interaction will be a main component of the
event. An open session will give participants the opportunity to present their own work in
progress in 5 minutes.
In principle, graduate students, doctoral students and postdocs from around the world will
be typical profiles of participants. However, there are no formal pre-requisites for
attendance in terms of academic degrees. Since there will be a variety of levels, specific
knowledge background may be assumed for some of the courses. DeepLearn 2017 is
also appropriate for more senior people who want to keep themselves updated on recent
developments and future trends. All will surely find it fruitful to listen and discuss with
major researchers, industry leaders and innovators.
In addition to keynotes, 3 courses will run in parallel during the whole event. Participants will be able to freely choose the courses they wish to attend as well as to move from one to another.
DeepLearn 2017 will take place in Bilbao, the largest city in the Basque Country, famous
for its gastronomy and the seat of the Guggenheim Museum. The venue will be:
DeustoTech, School of Engineering
University of Deusto
Avda. Universidades, 24
48014 Bilbao, Spain
Keynote speakers (to be completed)
Professors and courses (to be completed)
- Narendra Ahuja (University of Illinois, Urbana-Champaign), [introductory/intermediate] Basics of Deep Learning with Applications to Image Processing, Pattern Recognition and Computer Vision
- Pierre Baldi (University of California, Irvine), [intermediate/advanced] Deep Learning: Theory and Applications to the Natural Sciences
- Sven Behnke (University of Bonn), [intermediate] Visual Perception using Deep Convolutional Neural Networks
- Mohammed Bennamoun (University of Western Australia), [introductory/intermediate] Deep Learning for Computer Vision
- Hervé Bourlard (Idiap Research Institute), [intermediate/advanced] Deep Sequence Modeling: Historical Perspective and Current Trends
- Thomas Breuel (NVIDIA Corporation), Segmentation, Processing, and Tracking, with Applications to Video, Gaming, VR, and Self-driving Cars
- George Cybenko (Dartmouth College), [intermediate] Deep Learning of Behaviors
- Rina Dechter (University of California, Irvine), [introductory] Algorithms for Reasoning with Probabilistic Graphical Models
- Li Deng (Microsoft Research), tba
- Jianfeng Gao (Microsoft Research), [introductory/intermediate] An Introduction to Deep Learning for Natural Language Processing
- Michael Gschwind (IBM T.J. Watson Research Center), [introductory/intermediate] Deploying Deep Learning Applications at the Enterprise Scale
- Yufei Huang (University of Texas, San Antonio), [intermediate/advanced] Deep Learning for Bioinformatics
- Soo-Young Lee (Korea Advanced Institute of Science and Technology), Multi-modal Deep Learning for the Recognition of Human Emotions in the Real
- Li Erran Li (Columbia University), [intermediate/advanced] Deep Learning Security: Adversarial Examples and Adversarial Training
- Michael C. Mozer (University of Colorado, Boulder), [introductory/intermediate] Incorporating Domain Bias into Neural Networks
- Roderick Murray-Smith (University of Glasgow), [intermediate] Applications of Deep Learning Models in Human-Computer Interaction Research
- Hermann Ney (RWTH Aachen University), [intermediate/advanced] Speech Recognition and Machine Translation: From Statistical Decision Theory to Machine Learning and Deep Neural Networks
- Jose C. Principe (University of Florida), [intermediate/advanced] Cognitive Architectures for Object Recognition in Video
- Marc'Aurelio Ranzato (Facebook AI Research), [introductory/intermediate] Learning Representations for Vision, Speech and Text Processing Applications
- Maximilian Riesenhuber (Georgetown University), [introductory/intermediate] Deep Learning in the Brain
- Ruslan Salakhutdinov (Carnegie Mellon University), [intermediate/advanced] Foundations of Deep Learning and its Recent Advances
- Alessandro Sperduti (University of Padua), [intermediate/advanced] Deep Learning for Sequences
- Jimeng Sun (Georgia Institute of Technology), [introductory] Interpretable Deep Learning Models for Healthcare Applications
- Julian Togelius (New York University), [intermediate] (Deep) Learning for (Video) Games
- Raquel Urtasun (University of Toronto), tba
- Joos Vandewalle (KU Leuven), [introductory/intermediate] Data Processing Methods, and Applications of Least Squares Support Vector Machines
- Ying Nian Wu (University of California, Los Angeles), [introductory/intermediate] Deep Generative Models and Unsupervised Learning
- Eric P. Xing (Carnegie Mellon University), [intermediate/advanced] Statistical Machine Learning Perspectives of Extending Deep Neural Networks: Kernels, Logics, Regularizers, Priors, and Distributed Algorithms
- Georgios N. Yannakakis (University of Malta), [introductory/intermediate] Deep Learning for Games - But not for Playing them
- Scott Wen-tau Yih (Microsoft Research), [introductory/intermediate] Continuous Representations for Natural Language Understanding
- Richard Zemel (University of Toronto), [introductory/intermediate] Learning to Understand Images and Text
An open session will collect 5-minute voluntary presentations of work in progress by participants.
They should submit a half-page abstract containing title, authors, and summary of the
research to david.silva409 (at) yahoo.com by July 9, 2017.
- José Gaviria
- Carlos Martín(co-chair)
- Manuel Parra
- Iker Pastor
- Borja Sanz (co-chair)
- David Silva
It has to be done at http://grammars.grlmc.com/DeepLearn2017/registration.php.
The selection of up to 8 courses requested in the registration template is only tentative
and non-binding. For the sake of organization, it will be helpful to have an approximation
of the respective demand for each course.
Since the capacity of the venue is limited, registration requests will be processed on a first
come first served basis. The registration period will be closed and the on-line registration
facility disabled when the capacity of the venue will be complete. It is much recommended
to register prior to the event.
Suggestions for accommodation will be available on the website.
Participants will be delivered a certificate of attendance including the number of hours of
Questions and further information
david.silva409 (at) yahoo.com