January 21 - 25, 2019 in Lenk, Switzerland

Deep Learning

CUSO Winter School in Computer Science

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Welcome to the CUSO Winter School 2019 on Deep Learning!

The school takes place in Lenk, Switzerland, from January 21st to 25th, 2019. This school is organized under the Doctoral Program in Computer Science of the CUSO universities. It is primarily targeted at PhD students in computer science in these universities.

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40 Participants

Connect with researchers from all over Switzerland.

8 Lectures

Learn from leading experts in Deep Learning and Artificial Intelligence.

6 Speakers

Find out how they think one step ahead of everyone else.

Overview

Deep learning (DL) is today an extremely active research field, whose developments in the last 12 years have drastically increased the performance of data analysis methods and promise to bring further high-impact solutions to data-rich domains. Moreover, because many fields of research are becoming more and more data-driven and DL methods are data-agnostic, DL technologies see widespread application to academic research and the industry beyond computer science. As the scientific community is heavily investing efforts in DL, the field is progressing at an unprecedented pace and it is therefore imperative to have events (such as this Winter School), where the latest and most important results are presented in a structured format by leading researchers in the field.

Goal of the Winter School

In this CUSO Winter School we will bring together international experts in deep learning to share their expertise and provide a first-hand account of the fascinating developments and opportunities in this area. The goal of the school is twofold. First, the school will offer an introduction and overview of the research area for a broader audience, illustrating the relevance of the topic in scientific and commercial applications. In addition, for more advanced students it will foster interaction with renowned experts in the field, supporting them to become active and independent researchers on their own. We believe this winter school will address a broad audience of doctoral students and postdoctoral researchers at the CUSO universities, which are home to a number of research groups in related areas.

Venue

The winter school will take place in Lenk, an idyllic alpine resort in the Bernese Oberland. We will be located in the Hotel Kreuz. The hotel is within walking distance from the train station in Lenk.

Lenk is easily accessible via public transport. It is three hours by train from Zurich airport and three and a half hours from Geneva airport. Please visit the online timetable of the Swiss railways to find detailed train schedules. Simply enter "Lenk" as your destination.

Speakers

About Alexei Efros

Alexei (Alyosha) Efros is a professor in the EECS department at UC Berkeley. Prior to that, he on the faculty of Carnegie Mellon University, and has also been affiliated with École Normale Supérieure/INRIA and University of Oxford. His research is in the area of computer vision and computer graphics, especially at the intersection of the two. He is particularly interested in using data-driven techniques to tackle problems where large quantities of unlabeled visual data are readily available. Efros received his PhD in 2003 from UC Berkeley. He is a recipient of CVPR Best Paper Award (2006), NSF CAREER award (2006), Sloan Fellowship (2008), Guggenheim Fellowship (2008), Okawa Grant (2008), SIGGRAPH Significant New Researcher Award (2010), ECCV Best Paper Honorable Mention (2010), three Helmholtz Test-of-Time Prizes (1999,2003,2005), and the ACM Prize in Computing (2016).

About Rene Vidal

Rene Vidal is the Herschel Seder Professor of Biomedical Engineering and the Inaugural Director of the Mathematical Institute for Data Science at The Johns Hopkins University. He has secondary appointments in Computer Science, Electrical and Computer Engineering, and Mechanical Engineering. He is also a faculty member in the Center for Imaging Science (CIS), the Institute for Computational Medicine (ICM) and the Laboratory for Computational Sensing and Robotics (LCSR). Vidal's research focuses on the development of theory and algorithms for the analysis of complex high-dimensional datasets such as images, videos, time-series and biomedical data. His current major research focus is understanding the mathematical foundations of deep learning and its applications in computer vision and biomedical data science. His lab has pioneered the development of methods for dimensionality reduction and clustering, such as Generalized Principal Component Analysis and Sparse Subspace Clustering, and their applications to face recognition, object recognition, motion segmentation and action recognition. His lab creates new technologies for a variety of biomedical applications, including detection, classification and tracking of blood cells in holographic images, classification of embryonic cardio-myocytes in optical images, and assessment of surgical skill in surgical videos.

About Soumith Chintala

Soumith Chintala is a Researcher at Facebook AI Research, where he works on deep learning, reinforcement learning, generative image models, agents for video games and large-scale high-performance deep learning. Prior to joining Facebook in August 2014, he worked at MuseAmi, where he built deep learning models for music and vision targeted at mobile devices. He holds a Masters in CS from NYU, and spent time in Yann LeCun’s NYU lab building deep learning models for pedestrian detection, natural image OCR, depth-images among others.

About Jan Koutnik

My current research focuses on artificial neural networks, recurrent neural networks, evolutionary algorithms and deep-learning applied to reinforcement learning, control problems, image classification, handwriting and speech recognition. At IDSIA, I have been working on EU funded projects Humanobs and ``Nascence'', SNF project ``Theory and Practice in Reinforcement Learning 2'' as well as projects funded from industrial cooperation.

About Moustapha Cisse

Moustapha Cisse is a research scientist at Google. He is head of the Google AI center in Accra, Ghana where he leads research efforts in foundational machine learning and its applications to solving complex societal challenges. Moustapha is also a professor of machine learning at the African Institute of Mathematical Sciences and the founder and director of the African Masters of Machine Intelligence. He was previously a research scientist at Facebook AI Research. Before, he studied Mathematics and Physics at University Gaston Berger in Senegal and did his PhD at University Pierre and Marie Curie in France.

About Klaus Greff

Klaus Greff is a Ph.D. student at IDSIA under the supervision of Jürgen Schmidhuber. He did his masters degree in computer science at the University of Kaiserslautern.

Organizers

About Marcus Liwicki

Marcus Liwicki received his M.S. degree in Computer Science from the Free University of Berlin, Germany, in 2004, his PhD degree from the University of Bern, Switzerland, in 2007, and his habilitation degree at the Technical University of Kaiserslautern, Germany, in 2011. Currently he is chaired professor at Luleå University of Technology and a senior assistant in the University of Fribourg. His research interests include machine learning, pattern recognition, artificial intelligence, human computer interaction, digital humanities, knowledge management, ubiquitous intuitive input devices, document analysis, and graph matching. From October 2009 to March 2010 he visited Kyushu University (Fukuoka, Japan) as a research fellow (visiting professor), supported by the Japanese Society for the Promotion of Science. In 2015, at the young age of 32, he received the ICDAR young investigator award, a bi-annual award acknowledging outstanding achievements of in pattern recognition for researchers up to the age of 40.

About François Fleuret

François Fleuret got a PhD in Mathematics from INRIA and the University of Paris VI in 2000, and an Habilitation degree in Mathematics from the University of Paris XIII in 2006. He is the head of the Machine Learning group at the Idiap Research Institute, Switzerland, since 2007, and adjunct faculty in the School of Engineering of the École Polytechnique Fédérale de Lausanne since 2011, where he teaches machine learning. He has published more than 80 papers in peer-reviewed international conferences and journals. He is Associate Editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) since 2012, served as Area Chair for NIPS (2012, 2014, 2016, 2017, 2018) and ICCV (2012) and in the program committee of many top-tier international conferences in machine learning and computer vision. He is member of the Electrical Engineering Doctoral Program Committee at EPFL, and was or is expert for multiple funding agencies (Swiss National Science Foundation, European Research Council, Austrian Science Fund, Netherlands Organization for Scientific Research, French National Research Agency, Research Council of the Academy of Finland, US National Science Foundation). His main research interest is machine learning, with a particular focus on computational aspects and small sample learning, and applications in computer vision.

About Paolo Favaro

Paolo Favaro received the Laurea degree (B.Sc.+M.Sc.) from Università di Padova, Italy in 1999, and the M.Sc. and Ph.D. degree in electrical engineering from Washington University in St. Louis in 2003 and 2004 respectively. He was a postdoctoral researcher in the computer science department of the University of California, Los Angeles and subsequently in Cambridge University, UK. Between 2004 and 2006 he worked in medical imaging at Siemens Corporate Research, Princeton, USA. From 2006 to 2011 he was Lecturer and then Reader at Heriot-Watt University and Honorary Fellow at the University of Edinburgh, UK. In 2012 he became full professor at Universität Bern, Switzerland. His research interests are in computer vision, computational photography, machine learning, signal and image processing, estimation theory, inverse problems and variational techniques. He is also a member of the IEEE Society.

Schedule

The school will take place from Monday January 21st to Friday 25th at Hotel Kreuz in Lenk, Switzerland. Below is an overview of our planned schedule.

12.00 - 12:30

Arrival and registration at the hotel

12:30 - 13:00

Introduction

Paolo Favaro and François Fleuret

Welcome to the school and presentation of the schedule and activities

program
13:00 - 14:30

Theory of Deep Learning - Part I

by Rene Vidal

14:30 - 15:00

Coffee Break

15:00 - 16:30

Theory of Deep Learning - Part II

by Rene Vidal

17:00 - 20:00

Poster Session

by Attendees

20:15

Dinner

program
08:30 - 10:00

Background on Deep Learning - Part I

by François Fleuret

10:00 - 10:30

Coffee Break

10:30 - 12:00

Background on Deep Learning - Part II

by François Fleuret

12:15 - 13:00

Lunch

13:00 - 16:30

Free time for social activities

program
16:30 - 18:00

Applications - Part I

by Moustapha Cisse

18:00 - 18:30

Coffee Break

18:30 - 20:00

Applications - Part II

by Moustapha Cisse

20:15

Dinner

program
08:30 - 10:00

Unsupervised Learning - Part I

by Alyosha Efros

10:00 - 10:30

Coffe Break

10:30 - 12:00

Unsupervised Learning - Part II

by Alyosha Efros

12:15

Lunch

13:00 - 16:30

Free time for social activities

program
16:30 - 18:00

Recurrent Neural Networks

by Klaus Greff

18:00 - 18:30

Coffee Break

18:30 - 20:00

The Binding Problem

by Klaus Greff

20:15

Dinner

program
08:30 - 10:00

Generative Learning - Part I

by Soumith Chintala

10:00 - 10:30

Coffe Break

10:30 - 12:00

Generative Learning - Part II

by Soumith Chintala

12:15

Lunch

13:00 - 16:30

Free time for social activities

program
16:30 - 18:00

Reinforcement Learning - Part I

by Jan Koutnik

18:00 - 18:30

Coffee Break

18:30 - 20:00

Reinforcement Learning - Part II

by Jan Koutnik

20:15

Dinner

program
08:30 - 10:00

Project Presentations - Part I

Moderation by Paolo Favaro

Students present their group project

10:00 - 10:30

Coffe Break

10:30 - 12:00

Project Presentations - Part II

Moderation by Paolo Favaro

Students present their group project

12:15

Closure and departure

Registration

Location and Fees

The Winter School will take place at Hotel Kreuz in Lenk. Participants do not need to make their own reservation here. The Winter School automatically takes care of this.

Participants from the CUSO Doctoral Program in Computer Science are asked only to contribute a modest registration fee of Fr. 160.-. Accommodation costs for 4 nights in a double room and all meals are fully sponsored by the CUSO Doctoral Program. In addition, travel costs will be refunded for second class SBB tickets. No reimbursement will be made without the original tickets. You will get instructions during the school on how to get reimbursed.

If you are interested to register for the school but you are not affiliated with a CUSO university, please contact the organizers.

Registration

Participants affiliated with CUSO universities should register directly through the CUSO web page here. After registering for the winter school, wait for confirmation of your registration. Only then make the payment for the participation fee to the CUSO account. You will receive bank account details during the registration process.

Deadline

The registration deadline is the 21st of December, 2018.

Contact

For questions please contact Prof. Paolo Favaro, favaro@inf.unibe.ch, Prof. Marcus Liwicki marcus.liwicki@ltu.se or François Fleuret francois.fleuret@idiap.ch.