INTERNATIONAL SUMMER SCHOOL ON DEEP LEARNING
Bilbao, 17-21 Luglio 2017, International Summer School On Deep Learning
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, 30 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 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 academics and practitioners 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, 3-4 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)
Richard Socher (Salesforce), Tackling the Limits of Deep Learning
PROFESSORS AND COURSES:
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), [intermediate] 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 Precision Medicine and Biomedical informatics
Soo-Young Lee (Korea Advanced Institute of Science and Technology), [intermediate/advanced] Multi-modal Deep Learning for the Recognition of Human Emotions in the Wild
Li Erran Li (Columbia University), [intermediate/advanced] Deep Reinforcement Learning: Recent Advances and Frontiers
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
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] Generative Modeling 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.
A specific session will be devoted to demonstrations of practical uses of deep learning in industrial processes. Companies/people interested in contributing are welcome to submit a 1-page abstract containing the program of the demonstration, the duration requested and the logistics necessary. At least one of the people participating in the demonstration should have registered for the event. Expressions of interest have to be submitted to david.silva409 (at) yahoo.com by July 2, 2017.
Firms searching for personnel well skilled in deep learning will have a space reserved for one-to-one contacts. At least one of the people in charge of the search should have registered for the event. Expressions of interest have to be submitted to david.silva409 (at) yahoo.com by July 2, 2017.
Pablo García Bringas (co-chair)
Carlos Martín (co-chair)
Manuel Jesús Parra
Borja Sanz (co-chair)
It has to be done at
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.
Fees comprise access to all courses and lunches. There are several early registration deadlines. Fees depend on the registration deadline.
Suggestions for accommodation are available on the website.
Participants will be delivered a certificate of attendance indicating the number of hours of lectures.
QUESTIONS AND FURTHER INFORMATION:
david.silva409 (at) yahoo.com
Universidad de Deusto
Universitat Rovira i Virgili