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DeepLearn 2021 Winter: early registration August 21
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4th INTERNATIONAL SCHOOL ON DEEP LEARNING
DeepLearn 2021 Winter
Milan, Italy
January 11-15, 2021
Co-organized by:
Department of Information Engineering
Marche Polytechnic University
Institute for Research Development, Training and Advice – IRDTA
Brussels/London
https://irdta.eu/deeplearn2021w/
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--- Early registration deadline: August 21, 2020 ---
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In conjunction with ICPR 2020
https://www.micc.unifi.it/icpr2020/
SCOPE:
DeepLearn 2021 Winter will be a research training event with a
global scope aiming at updating participants on the most recent
advances in the critical and fast developing area of deep
learning. Previous events were held in Bilbao, Genova and Warsaw.
Deep learning is a branch of artificial intelligence covering a
spectrum of current exciting 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 24 four-hour and a half courses and 3 keynote
lectures, 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 2021 Winter 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.
VENUE:
DeepLearn 2021 Winter will take place in Milan, the third largest
economy among European cities and one of the Four Motors for
Europe. The venue will be:
MiCo Milano Convention Centre
Piazzale Carlo Magno 1
Milan
https://www.micomilano.it/it/
The venue will be shared with the 25th International Conference on
Pattern Recognition – ICPR 2020
https://www.micc.unifi.it/icpr2020/
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.
KEYNOTE SPEAKERS:
Nello Cristianini (University of Bristol), Data, Intelligence and
Shortcuts
Petia Radeva (University of Barcelona), Uncertainty Modeling and
Deep Learning in Food Analysis
Indrė Žliobaitė (University of Helsinki), Any Hope for Deep
Learning in Deep Time?
PROFESSORS AND COURSES:
Ignacio Arganda-Carreras (University of the Basque Country),
[introductory/intermediate] Deep Learning for Bioimage Analysis
Mikhail Belkin (Ohio State University), [intermediate/advanced]
Understanding Deep Learning through the Lens of
Over-parameterization
Thomas G. Dietterich (Oregon State University), [introductory]
Machine Learning Methods for Robust Artificial Intelligence
Georgios Giannakis (University of Minnesota), [advanced] Ensembles
for Interactive and Deep Learning Machines with Scalability,
Expressivity, and Adaptivity
Sergei V. Gleyzer (University of Alabama),
[introductory/intermediate] Machine Learning Fundamentals and
Their Applications to Very Large Scientific Data: Rare Signal and
Feature Extraction, End-to-end Deep Learning, Uncertainty
Estimation and Realtime Machine Learning Applications in Software
and Hardware
Çağlar Gülçehre (DeepMind), [intermediate/advanced] Deep
Reinforcement Learning
Balázs Kégl (Huawei Technologies), [introductory] Deep Model-based
Reinforcement Learning
Ludmila Kuncheva (Bangor University), [intermediate] Classifier
Ensembles in the Era of Deep Learning
Vincent Lepetit (ENPC ParisTech), [intermediate] Deep Learning and
3D Geometry
Geert Leus (Delft University of Technology),
[introductory/intermediate] Graph Signal Processing: Introduction
and Connections to Distributed Optimization and Deep Learning
Andy Liaw (Merck Research Labs), [introductory] Deep Learning and
Statistics: Better Together
Debora Marks (Harvard Medical School), [intermediate] Protein
Design Using Deep Learning
Abdelrahman Mohamed (Facebook AI Research),
[introductory/advanced] Recent Advances in Automatic Speech
Recognition
Sayan Mukherjee (Duke University), [introductory/intermediate]
Integrating Deep Learning with Statistical Modeling
Hermann Ney (RWTH Aachen University), [intermediate/advanced]
Speech Recognition and Machine Translation: From Statistical
Decision Theory to Machine Learning and Deep Neural Networks
Lyle John Palmer (University of Adelaide), [introductory/advanced]
Epidemiology for Machine Learning Investigators
Razvan Pascanu (DeepMind), [intermediate/advanced] Understanding
Learning Dynamics in Deep Learning and Deep Reinforcement Learning
Jan Peters (Technical University of Darmstadt), [intermediate]
Robot Learning
José C. Príncipe (University of Florida), [intermediate/advanced]
Cognitive Architectures for Object Recognition in Video
Björn W. Schuller (Imperial College London),
[introductory/intermediate] Deep Signal Processing
Sargur N. Srihari (University at Buffalo), [introductory]
Generative Models in Deep Learning
Gaël Varoquaux (INRIA), [intermediate] Representation Learning in
Limited Data Settings
René Vidal (Johns Hopkins University), [intermediate/advanced]
Mathematics of Deep Learning
Ming-Hsuan Yang (University of California, Merced),
[intermediate/advanced] Learning to Track Objects
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 the title, authors, and summary of the
research to
david@irdta.eu by January 3, 2021.
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. People
participating in the demonstration must register for the event.
Expressions of interest have to be submitted to
david@irdta.eu by
January 3, 2021.
EMPLOYER SESSION:
Firms searching for personnel well skilled in deep learning will
have a space reserved for one-to-one contacts. It is recommended
to produce a 1-page .pdf leaflet with a brief description of the
company and the profiles looked for to be circulated among the
participants prior to the event. People in charge of the search
must register for the event. Expressions of interest have to be
submitted to
david@irdta.eu by January 3, 2021.
ORGANIZING COMMITTEE:
Emanuele Frontoni (Ancona, co-chair)
Carlos Martín-Vide (Tarragona, program chair)
Sara Moccia (Ancona)
Sara Morales (Brussels)
Marina Paolanti (Ancona)
Manuel J. Parra-Royón (Granada)
Luca Romeo (Ancona)
David Silva (London, co-chair)
REGISTRATION:
It has to be done at
https://irdta.eu/deeplearn2021w/registration/
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
tool 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 will be available in due time.
CERTIFICATE:
A certificate of successful participation in the event will be
delivered indicating the number of hours of lectures.
QUESTIONS AND FURTHER INFORMATION:
david@irdta.eu
ACKNOWLEDGMENTS:
Dipartimento di Ingegneria dell'Informazione, Università
Politecnica delle Marche
Institute for Research Development, Training and Advice – IRDTA,
Brussels/London