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Institution

Helsinki Institute for Information Technology

FacilityEspoo, Finland
About: Helsinki Institute for Information Technology is a facility organization based out in Espoo, Finland. It is known for research contribution in the topics: Population & Bayesian network. The organization has 630 authors who have published 1962 publications receiving 63426 citations.


Papers
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Book ChapterDOI
16 Mar 2009
TL;DR: In this paper, the authors show how MRF potentials can be estimated using Score Matching (SM) to learn filters of size 12 × 12 pixels, considerably larger than traditional "hand-crafted" MRFs potentials.
Abstract: Markov Random Field (MRF) models with potentials learned from the data have recently received attention for learning the low-level structure of natural images. A MRF provides a principled model for whole images, unlike ICA, which can in practice be estimated for small patches only. However, learning the filters in an MRF paradigm has been problematic in the past since it required computationally expensive Monte Carlo methods. Here, we show how MRF potentials can be estimated using Score Matching (SM). With this estimation method we can learn filters of size 12 ×12 pixels, considerably larger than traditional "hand-crafted" MRF potentials. We analyze the tuning properties of the filters in comparison to ICA filters, and show that the optimal MRF potentials are similar to the filters from an overcomplete ICA model.

26 citations

Proceedings ArticleDOI
20 Aug 2005
TL;DR: It is shown that despite the deep-going controversies, both camps benefit from considering the alternative approach and a middle ground can be found.
Abstract: During the last few years, there have been debates over what is context and how computers should act upon it. Two disparate camps of thought can be recognized. First, Realism, having its roots in natural sciences, believes that contexts exist out there and that, if properly instrumented and programmed, computers can correctly recognize and adapt to them. Second, Constructivism, having its roots in human and social sciences, believes that contexts are human creations, mental and social, and that computers ought to provide resources for managing them. We reveal some fundamental differences between the two in three different application domains. We show that despite the deep-going controversies, both camps benefit from considering the alternative approach and a middle ground can be found.

26 citations

Proceedings Article
01 Jan 2009
TL;DR: This paper presents a succinct way of representing data on the basis of itemsets that identify strong interactions, using entropy-based elements for the data description, and presents two algorithms that provide high-quality descriptions of the data in terms of strongly interacting variables.
Abstract: Most pattern discovery algorithms easily generate very large numbers of patterns, making the results impossible to understand and hard to use Recently, the problem of instead selecting a small subset of informative patterns from a large collection of patterns has attracted a lot of interest In this paper we present a succinct way of representing data on the basis of itemsets that identify strong interactions This new approach, LESS, provides a more powerful and more general technique to data description than existing approaches Low-entropy sets consider the data symmetrically and as such identify strong interactions between attributes, not just between items that are present Selection of these patterns is executed through the MDL-criterion This results in only a handful of sets that together form a compact lossless description of the data By using entropy-based elements for the data description, we can successfully apply the maximum likelihood principle to locally cover the data optimally Further, it allows for a fast, natural and well performing heuristic Based on these approaches we present two algorithms that provide high-quality descriptions of the data in terms of strongly interacting variables Experiments on these methods show that high-quality results are mined: very small pattern sets are returned that are easily interpretable and understandable descriptions of the data, and can be straightforwardly visualized Swap randomization experiments and high compression ratios show that they capture the structure of the data well

26 citations

Journal ArticleDOI
TL;DR: Copying of articles that carry a code at the bottom of the first or last page or screen display, copying is permitted provided that the per-copy fee indicated in the code is paid through the Copyright Clearance Center.
Abstract: ing with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or fee. Request permission to publish from: Publications Dept., ACM, Inc., Fax +1-212-869-0481 or email permissions@acm.org For other copying of articles that carry a code at the bottom of the first or last page or screen display, copying is permitted provided that the per-copy fee indicated in the code is paid through the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, +1-978-750-8400, +1-978-750-4470 (fax). A BIMONTHLy PUBLICATION OF ACM in te ra c ti o n s M a rc h + A p ri l 2 0 0 8

26 citations

Book ChapterDOI
26 Aug 2015
TL;DR: A reference architectural model is studied that aims at intelligent utilization of personal mobile data in generic health services and employs the smart spaces paradigm with its prominent technologies adopted from the Internet of Things (IoT) and Semantic Web.
Abstract: Mobile health (m-Health) scenarios form an important direction for enhancing “traditional” healthcare systems. The latter implement backend services for use primarily by medical personnel and typically at hospitals. Current development meets with the challenge of personal data inclusion to the whole healthcare system with subsequent “smart” service construction and delivery. This paper makes a step towards the concept development of intelligence support in personalized m-Health systems. We study a reference architectural model that aims at intelligent utilization of personal mobile data in generic health services. Each personalized m-Health system contains the patient’s medical sensor network (MSN). To support the service intelligence we employ the smart spaces paradigm with its prominent technologies adopted from the Internet of Things (IoT) and Semantic Web.

26 citations


Authors

Showing all 632 results

NameH-indexPapersCitations
Dimitri P. Bertsekas9433285939
Olli Kallioniemi9035342021
Heikki Mannila7229526500
Jukka Corander6641117220
Jaakko Kangasjärvi6214617096
Aapo Hyvärinen6130144146
Samuel Kaski5852214180
Nadarajah Asokan5832711947
Aristides Gionis5829219300
Hannu Toivonen5619219316
Nicola Zamboni5312811397
Jorma Rissanen5215122720
Tero Aittokallio522718689
Juha Veijola5226119588
Juho Hamari5117616631
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20231
20224
202185
202097
2019140
2018127