[Om-announce] DeepLearn 2018: early registration April 7

Mon Apr 2 17:45:41 CEST 2018

DeepLearn 2018: early registration April 7*To be removed from our mailing list, please respond to this message with UNSUBSCRIBE in the subject line*

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2nd INTERNATIONAL SUMMER SCHOOL ON DEEP LEARNING

DeepLearn 2018

Genova, Italy

July 23-27, 2018

Organized by:

University of Genova

IRDTA – Brussels/London

http://grammars.grlmc.com/DeepLearn2018/

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--- Early registration deadline: April 7, 2018 ---

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SCOPE:

DeepLearn 2018 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 neurosciences, computer vision, speech recognition, language processing, human-computer interaction, drug discovery, biomedical informatics, healthcare, 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 2 keynote lectures, 24 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. Moreover, there will be two special sessions with industrial and recruitment profiles.

ADDRESSED TO:

Master's students, PhD students, postdocs, and industry practitioners 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. Overall, DeepLearn 2018 is addressed to students, researchers and practitioners who want to keep themselves updated about recent developments and future trends. All will surely find it fruitful to listen and discuss with major researchers, industry leaders and innovators.

STRUCTURE:

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.

VENUE:

DeepLearn 2018 will take place in Genova, the capital city of Liguria, inscribed on the UNESCO World Heritage List and with one of the most important ports of the Mediterranean. The venue will be:

Porto Antico di Genova – Centro Congressi

Magazzini del Cotone – Module 10

16128 Genova, Italy

KEYNOTE SPEAKERS:

tba

PROFESSORS AND COURSES: (to be completed)

Pierre Baldi (University of California, Irvine), [intermediate/advanced] Deep Learning: Theory, Algorithms, and Applications to the Natural Sciences

Thomas Breuel (NVIDIA Corporation), [intermediate] Design and Implementation of Deep Learning Applications

Joachim M. Buhmann (Swiss Federal Institute of Technology Zurich), [introductory/advanced] Model Selection by Algorithm Validation

Li Deng (Citadel), tba

Sergei V. Gleyzer (University of Florida), [introductory/intermediate] Feature Extraction, End-end Deep Learning and Applications to Very Large Scientific Data: Rare Signal Extraction, Uncertainty Estimation and Realtime Machine Learning Applications in Software and Hardware

Michael Gschwind (IBM Global Chief Data Office), [introductory/intermediate] Deploying Deep Learning at Enterprise Scale

Xiaodong He (Microsoft Research), [intermediate/advanced] Deep Learning for Natural Language Processing and Language-Vision Multimodal Intelligence

Namkug Kim (Asan Medical Center), [intermediate] Deep Learning for Computer Aided Detection/Diagnosis in Radiology and Pathology

Sun-Yuan Kung (Princeton University), [introductory] Systematic (Analytical and Empirical) Optimization/Generalization of  Deep Learning Networks

Li Erran Li (Uber ATG), [intermediate/advanced] Deep Reinforcement Learning: Foundations, Recent Advances and Frontiers

Dimitris N. Metaxas (Rutgers University), [advanced] Adversarial, Discriminative, Recurrent, and Scalable Deep Learning Methods for Human Motion Analytics, Medical Image Analysis, Scene Understanding and Image Generation

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), [introductory/advanced] Cognitive Architectures for Object Recognition in Video

Björn Schuller (Imperial College London), [intermediate/advanced] Deep Learning for Signal Analysis

Michèle Sebag (French National Center for Scientific Research, Gif-sur-Yvette), [intermediate] Representation Learning, Domain Adaptation and Generative Models with Deep Learning

Ponnuthurai N Suganthan (Nanyang Technological University), [introductory/intermediate] Learning Algorithms for Classification, Forecasting and Visual Tracking

Johan Suykens (KU Leuven), [introductory/intermediate] Deep Learning and Kernel Machines

Kenji Suzuki (Tokyo Institute of Technology), [introductory/advanced] Deep Learning in Medical Image Processing, Analysis and Diagnosis

Gökhan Tür (Google Research), [intermediate/advanced] Deep Learning in Conversational AI

Eric P. Xing (Carnegie Mellon University), [intermediate/advanced] A Statistical Machine Learning Perspective of Deep Learning: Algorithm, Theory, Scalable Computing

Ming-Hsuan Yang (University of California, Merced), [intermediate/advanced] Learning to Track Objects

Yudong Zhang (Nanjing Normal University), [introductory/intermediate] Convolutional Neural Network and Its Variants

OPEN SESSION:

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 at irdta.eu by July 15, 2018.

INDUSTRIAL SESSION:

A session will be devoted to 10-minute demonstrations of practical applications of deep learning in industry. Companies interested in contributing are welcome to submit a 1-page abstract containing the program of the demonstration and the logistics needed. At least one of the people participating in the demonstration must register for the event. Expressions of interest have to be submitted to david at irdta.eu by July 15, 2018.

EMPLOYERS SESSION:

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 must register for the event. Expressions of interest have to be submitted to david at irdta.eu by July 15, 2018.

ORGANIZING COMMITTEE:

Francesco Masulli (Genova, co-chair)

Sara Morales (Brussels)

Manuel J. Parra-Royón (Granada)

David Silva (London, co-chair)

REGISTRATION:

It has to be done at

http://grammars.grlmc.com/DeepLearn2018/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 estimation of the respective demand for each course. During the event, participants will be free to attend the courses they wish.

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 is exhausted. It is highly recommended to register prior to the event.

FEES:

Fees comprise access to all courses and lunches. There are several early registration deadlines. Fees depend on the registration deadline.

ACCOMMODATION:

Suggestions for accommodation can be found at

http://www.deeplearn-hotels.promoest.com/hp.aspx?s=0

CERTIFICATE:

A certificate of successful participation in the event will be delivered indicating the number of hours of lectures.

QUESTIONS AND FURTHER INFORMATION:

david at irdta.eu

ACKNOWLEDGMENTS:

Università degli studi di Genova

Institute for Research Development, Training and Advice (IRDTA) – Brussels/London

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