NOBIM 2021 - program. Register by 30th August.
Kjersti Engan
Dear Nobim members
NOBIM 2021 will be held as a physical conference 13th-14th September, Gardermoen, Norway.
Information and registration at nobim.no
Venue: Radisson Blu Airport Hotel, Oslo Gardermoen
Registration deadline: 30th August
Registration fees (NOK) include lunch for all, both days. Hotel option includes overnight stay 13-14th sept. and dinner in addition:
|
|
To hotel at arrival |
To Nobim after registration |
|
student* with accommodation: |
2800 |
0 |
|
student* without accommodation |
1600 |
0 |
|
Member (non-student) with accommodation |
2800 |
300 |
|
Member without accommodation |
1600 |
300 |
|
Non-member** with accommodation |
2800 |
600 |
|
Non-member** without accommodation |
1600 |
600 |
*Students who present their work will get a reimbursement if they become members in Nobim ( up to 2800 for master students, 1000 for PhD students)
**Membership in Nobim costs 200 NOK a year (free for Masterstudents).
The program is attached to the email.
We are very happy to have with us two internationally recognized key-note speakers, talks and bio presented under.
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Key Note speakers – NOBIM 2021
Prof. Pier Luigi Dragotti, Imperial College London
“Sparsity and Deep Neural Networks: a match made in Heaven”
Abstract: Deep Neural Networks are currently able to achieve state-of-the-art performance in many imaging tasks including image denoising and image super-resolution. At the same time sparse representation theory has been central to many fundamental developments in image processing over the last two decades. In this talk, we argue that we can do far better in choosing deep-learning architectures by relying systematically on ideas in sparse representation and that this approach leads to deep neural networks which are easier to interpret.
In the first part of the talk, we propose an approach based on deep dictionary learning to develop deep neural network for image super-resolution. The proposed architecture contains several layers of analysis dictionaries
to extract high-level features and one synthesis dictionary which is designed to optimize the reconstruction task. Each analysis dictionary contains two sub-dictionaries: an information preserving analysis dictionary (IPAD) and a clustering analysis dictionary
(CAD). The IPAD with its corresponding thresholds passes the key information from the previous layer, while the CAD with its properly designed thresholds provides a sparse representation of input data that facilitates discrimination of key features.
We then look at the multi-modal case and use the dictionary learning framework as a tool to model dependency across modality, to dictate the architecture of a deep neural network and to initialize the parameters of the network. Numerical results show that this approach leads to state-of-the-art results.
Biography: Pier
Luigi Dragotti is Professor of Signal Processing in the Electrical and Electronic Engineering Department at Imperial College London and a Fellow of the IEEE. He received the Laurea Degree (summa cum laude) in Electronic Engineering from the University Federico
II, Naples, Italy, in 1997; the Master degree in Communications Systems from the Swiss Federal Institute of Technology of Lausanne (EPFL), Switzerland in 1998; and PhD degree from EPFL, Switzerland, in 2002. He has held several visiting positions. In particular,
he was a visiting student at Stanford University, Stanford, CA in 1996, a summer researcher in the Mathematics of Communications Department at Bell Labs, Lucent Technologies, Murray Hill, NJ in 2000, a visiting scientist at Massachusetts Institute of Technology
(MIT) in 2011 and a visiting scholar at Trinity College Cambridge in 2020.
Dragotti was Editor-in-Chief of the IEEE Transactions on Signal Processing (2018-2020), Technical Co-Chair for the European Signal Processing Conference in 2012, Associate Editor of the IEEE Transactions on Image Processing
from 2006 to 2009. He was also Elected Member of the IEEE Computational Imaging Technical Committee and the recipient of an ERC starting investigator award for the project RecoSamp. Currently, he is IEEE SPS Distinguished Lecturer.
His research interests include sampling theory, wavelet theory and its applications, computational imaging and sparsity-driven signal processing.
Assoc. Prof. Veronika Cheplygina, ITU Copenhagen
“Cats, crowds and other considerations for learning with limited labelled data”
Abstract: Machine learning has vast potential in medical image analysis, improving possibilities for early diagnosis and prognosis of disease. Algorithms typically need large amounts of representative, annotated examples for good performance, which may be difficult to achieve, for example due to differences between image acquisition procedures, or the time and effort involved in annotation. To address these problems, several approaches have been proposed, which are either aimed at adapting to use other types of annotated data, and or at gathering annotations more efficiently.
In this talk I will highlight two such approaches: transfer learning from natural images such as cats, and crowdsourcing by annotators without medical expertise. I will also discuss more general issues we face we as a community face when addressing such problems.
Biography: Dr.
Veronika Cheplygina's research focuses on limited labeled scenarios in machine learning, in particular in medical image analysis. She received her Ph.D. from Delft University of Technology in 2015. After a postdoc at the Erasmus Medical Center, in 2017 she
started as an assistant professor at Eindhoven University of Technology. In 2020, failing to achieve various metrics, she left the tenure track of search of the next step where she can contribute to open and inclusive science. She recently started as an associate professor
at IT University of Copenhagen.
Next to research and teaching, Veronika blogs about academic life on https://www.veronikach.com, and gives talks and workshops on failure and related topics. She also loves cats, which you will often encounter in her work.