<!doctype html>
<html>
<head>
<meta charset="UTF-8">
</head>
<body>
<div style="" class="default-style">
<div class="default-style">
******************************************************************
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
5th INTERNATIONAL SCHOOL ON DEEP LEARNING
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
DeepLearn 2022 Winter
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Bournemouth, UK
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
January 17-21, 2022
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
https://irdta.eu/deeplearn/2022wi/
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
***********
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Co-organized by:
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Department of Computing and Informatics
<br>
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Bournemouth University
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Institute for Research Development, Training and Advice – IRDTA Brussels/London
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
******************************************************************
</div>
<div class="default-style">
Early registration: September 15, 2021
</div>
<div class="default-style">
******************************************************************
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
SCOPE:
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
DeepLearn 2022 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, Warsaw and Las Palmas de Gran Canaria.
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
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 a huge variety of different environments: computer vision, neurosciences, speech recognition, language processing, human-computer interaction, drug discovery, biomedical informatics, image analysis, recommender systems, advertising, fraud detection, robotics, games, etc. etc. Renowned academics and industry pioneers will lecture and share their views with the audience.
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
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. Face to face interaction and networking will be main components of the event.
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
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.
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
ADDRESSED TO:
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Graduate students, postgraduate students and industry practitioners will be typical profiles of participants. However, there are no formal pre-requisites for attendance in terms of academic degrees, so people less or more advanced in their career will be welcome as well. Since there will be a variety of levels, specific knowledge background may be assumed for some of the courses. Overall, DeepLearn 2022 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 to and discuss with major researchers, industry leaders and innovators.
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
VENUE:
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
DeepLearn 2022 Winter will take place in Bournemouth, a coastal resort town on the south coast of England. The venue will be:
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
TBA
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
STRUCTURE:
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
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.
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Full in vivo online participation will be possible. However, the organizers want to emphasize the importance of face to face interaction and networking in this kind of research training event.
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
KEYNOTE SPEAKERS:
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Yi Ma (University of California, Berkeley), White-box Deep (Convolution) Networks from the Principle of Rate Reduction
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Daphna Weinshall (Hebrew University of Jerusalem), Curriculum Learning in Deep Networks
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Eric P. Xing (Carnegie Mellon University), It Is Time for Deep Learning to Understand Its Expense Bills
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
PROFESSORS AND COURSES:
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Peter L. Bartlett (University of California, Berkeley), [intermediate/advanced] Deep Learning: A Statistical Viewpoint
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Joachim M. Buhmann (Swiss Federal Institute of Technology, Zürich), [introductory/advanced] Algorithm Validation for Data Science
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Nitesh Chawla (University of Notre Dame), [introductory/intermediate] Graph Representation Learning
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Seungjin Choi (BARO AI Academy), [introductory/intermediate] Bayesian Optimization over Continuous, Discrete, or Hybrid Spaces
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Sumit Chopra (New York University), [intermediate] Deep Learning in Healthcare
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Rüdiger Dillmann (Karlsruhe Institute of Technology), [introductory/intermediate] Building Brains for Robots
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Marco Duarte (University of Massachusetts, Amherst), [introductory/intermediate] Explainable Machine Learning
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Charles Elkan (University of California, San Diego), [intermediate] AI and ML Applications in Finance and Retail
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Rob Fergus (New York University), [intermediate/advanced] Self-supervised Learning of Visual Representations for Recognition and Interaction
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
João Gama (University of Porto), [introductory] Learning from Data Streams: Challenges, Issues, and Opportunities
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Claus Horn (Zurich University of Applied Sciences), [intermediate] Deep Learning for Biotechnology
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Nathalie Japkowicz (American University), [intermediate/advanced] Learning from Class Imbalances
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Gregor Kasieczka (University of Hamburg), [introductory/intermediate] Deep Learning Fundamental Physics: Rare Signals, Unsupervised Anomaly Detection, and
</div>
<div class="default-style">
Generative Models
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Karen Livescu (Toyota Technological Institute at Chicago), [intermediate/advanced] Speech Processing: Automatic Speech Recognition and beyond
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
David McAllester (Toyota Technological Institute at Chicago), [intermediate/advanced] Information Theory for Deep Learning
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Dhabaleswar K. Panda (Ohio State University), [intermediate] Exploiting High-performance Computing for Deep Learning: Why and How?
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Tomaso Poggio (Massachusetts Institute of Technology), [advanced] Deep Learning: Theoretical Observations
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Fabio Roli (University of Cagliari), [introductory/intermediate] Adversarial Machine Learning
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Jude W. Shavlik (University of Wisconsin, Madison), [introductory/intermediate] Advising, Explaining, Distilling, and Quantizing Deep Neural Networks
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Kunal Talwar (Apple), [introductory/intermediate] Foundations of Differentially Private Learning
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Tinne Tuytelaars (KU Leuven), [introductory/intermediate] Continual Learning in Deep Neural Networks
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Lyle Ungar (University of Pennsylvania), [intermediate] Natural Language Processing using Deep Learning
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Yu-Dong Zhang (University of Leicester), [introductory/intermediate] Convolutional Neural Networks and Their Applications to COVID-19 Diagnosis
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
OPEN SESSION:
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
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 9, 2022.
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
INDUSTRIAL SESSION:
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
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 in charge of the demonstration must register for the event. Expressions of interest have to be submitted to david@irdta.eu by January 9, 2022.
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
EMPLOYER SESSION:
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
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 9, 2022.
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
ORGANIZING COMMITTEE:
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Rashid Bakirov (Bournemouth, co-chair)
<br>
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Nan Jiang (Bournemouth, co-chair)
<br>
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Carlos Martín-Vide (Tarragona, program chair)
<br>
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Sara Morales (Brussels)
<br>
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
David Silva (London, co-chair)
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
REGISTRATION:
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
It has to be done at
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
https://irdta.eu/deeplearn/2022wi/registration/
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
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.
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
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 will get exhausted. It is highly recommended to register prior to the event.
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
FEES:
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Fees comprise access to all courses and lunches. There are several early registration deadlines. Fees depend on the registration deadline.
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
ACCOMMODATION:
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Accommodation suggestions will be available in due time at
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
https://irdta.eu/deeplearn/2022wi/accommodation/
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
CERTIFICATE:
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
A certificate of successful participation in the event will be delivered indicating the number of hours of lectures.
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
QUESTIONS AND FURTHER INFORMATION:
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
david@irdta.eu
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
ACKNOWLEDGMENTS:
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Bournemouth University
</div>
<div class="default-style">
<br>
</div>
<div class="default-style">
Institute for Research Development, Training and Advice – IRDTA, Brussels/London
<br>
</div>
</div>
</body>
</html>