3 Search Results for "Leal-Taix�, Laura"

Document
DOI: 10.4230/OASIcs.LDK.2021.8
Plenary Debates of the Parliament of Finland as Linked Open Data and in Parla-CLARIN Markup

Authors: Laura Sinikallio, Senka Drobac, Minna Tamper, Rafael Leal, Mikko Koho, Jouni Tuominen, Matti La Mela, and Eero Hyvönen

Published in: OASIcs, Volume 93, 3rd Conference on Language, Data and Knowledge (LDK 2021)

Abstract
This paper presents a knowledge graph created by transforming the plenary debates of the Parliament of Finland (1907-) into Linked Open Data (LOD). The data, totaling over νm{900 000} speeches, with automatically created semantic annotations and rich ontology-based metadata, are published in a Linked Open Data Service and are used via a SPARQL API and as data dumps. The speech data is part of larger LOD publication FinnParla that also includes prosopographical data about the politicians. The data is being used for studying parliamentary language and culture in Digital Humanities in several universities. To serve a wider variety of users, the entirety of this data was also produced using Parla-CLARIN markup. We present the first publication of all Finnish parliamentary debates as data. Technical novelties in our approach include the use of both Parla-CLARIN and an RDF schema developed for representing the speeches, integration of the data to a new Parliament of Finland Ontology for deeper data analyses, and enriching the data with a variety of external national and international data sources.
Cite as

Laura Sinikallio, Senka Drobac, Minna Tamper, Rafael Leal, Mikko Koho, Jouni Tuominen, Matti La Mela, and Eero Hyvönen. Plenary Debates of the Parliament of Finland as Linked Open Data and in Parla-CLARIN Markup. In 3rd Conference on Language, Data and Knowledge (LDK 2021). Open Access Series in Informatics (OASIcs), Volume 93, pp. 8:1-8:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{sinikallio_et_al:OASIcs.LDK.2021.8,
  author =	{Sinikallio, Laura and Drobac, Senka and Tamper, Minna and Leal, Rafael and Koho, Mikko and Tuominen, Jouni and La Mela, Matti and Hyv\"{o}nen, Eero},
  title =	{{Plenary Debates of the Parliament of Finland as Linked Open Data and in Parla-CLARIN Markup}},
  booktitle =	{3rd Conference on Language, Data and Knowledge (LDK 2021)},
  pages =	{8:1--8:17},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-199-3},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{93},
  editor =	{Gromann, Dagmar and S\'{e}rasset, Gilles and Declerck, Thierry and McCrae, John P. and Gracia, Jorge and Bosque-Gil, Julia and Bobillo, Fernando and Heinisch, Barbara},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.LDK.2021.8},
  URN =		{urn:nbn:de:0030-drops-145444},
  doi =		{10.4230/OASIcs.LDK.2021.8},
  annote =	{Keywords: Plenary debates, parliamentary data, Parla-CLARIN, Linked Open Data, Digital Humanities}
}
Document
DOI: 10.4230/DagRep.7.9.109
Deep Learning for Computer Vision (Dagstuhl Seminar 17391)

Authors: Daniel Cremers, Laura Leal-Taixé, and René Vidal

Published in: Dagstuhl Reports, Volume 7, Issue 9 (2018)

Abstract
The field of computer vision engages in the goal to enable and enhance a machine’s ability to infer knowledge and information from spatial and visual input data. Recent advances in data-driven learning approaches, accelerated by increasing parallel computing power and a ubiquitous availability of large amounts of data, pushed the boundaries of almost every vision related subdomain. The most prominent example of these machine learning approaches is a so called deep neural network (DNN), which works as a general function approximator and can be trained to learn a mapping between given input and target output data. Research on and with these DNN is generally referred to as Deep Learning. Despite its high dimensional and complex input space, research in the field of computer vision was and still is one of the main driving forces for new development in machine and deep learning, and vice versa. This seminar aims to discuss recent works on theoretical and practical advances in the field of deep learning itself as well as state-of-the-art results achieved by applying learning based approaches to various vision problems. Our diverse spectrum of topics includes theoretical and mathematical insights focusing on a better understanding of the fundamental concepts behind deep learning and a multitude of specific research topics facilitating an exchange of knowledge between peers of the research community.
Cite as

Daniel Cremers, Laura Leal-Taixé, and René Vidal. Deep Learning for Computer Vision (Dagstuhl Seminar 17391). In Dagstuhl Reports, Volume 7, Issue 9, pp. 109-125, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@Article{cremers_et_al:DagRep.7.9.109,
  author =	{Cremers, Daniel and Leal-Taix\'{e}, Laura and Vidal, Ren\'{e}},
  title =	{{Deep Learning for Computer Vision (Dagstuhl Seminar 17391)}},
  pages =	{109--125},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2018},
  volume =	{7},
  number =	{9},
  editor =	{Cremers, Daniel and Leal-Taix\'{e}, Laura and Vidal, Ren\'{e}},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.7.9.109},
  URN =		{urn:nbn:de:0030-drops-85912},
  doi =		{10.4230/DagRep.7.9.109},
  annote =	{Keywords: computer vision, convolutional networks, deep learning, machine learning}
}
Document
DOI: 10.4230/DagRep.5.2.80
Holistic Scene Understanding (Dagstuhl Seminar 15081)

Authors: Jiri Matas, Vittorio Murino, Bodo Rosenhahn, and Laura Leal-Taixé

Published in: Dagstuhl Reports, Volume 5, Issue 2 (2015)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 15081 "Holistic Scene Understanding". During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Overall, the seminar was a great success, which is also reflected in the very positive feedback we received from the evaluation.
Cite as

Jiri Matas, Vittorio Murino, Bodo Rosenhahn, and Laura Leal-Taixé. Holistic Scene Understanding (Dagstuhl Seminar 15081). In Dagstuhl Reports, Volume 5, Issue 2, pp. 80-108, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)

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@Article{matas_et_al:DagRep.5.2.80,
  author =	{Matas, Jiri and Murino, Vittorio and Rosenhahn, Bodo and Leal-Taix\'{e}, Laura},
  title =	{{Holistic Scene Understanding (Dagstuhl Seminar 15081)}},
  pages =	{80--108},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2015},
  volume =	{5},
  number =	{2},
  editor =	{Matas, Jiri and Murino, Vittorio and Rosenhahn, Bodo and Leal-Taix\'{e}, Laura},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.5.2.80},
  URN =		{urn:nbn:de:0030-drops-50479},
  doi =		{10.4230/DagRep.5.2.80},
  annote =	{Keywords: Scene Analysis, Image Understanding, Crowd Analysis, People and Object Recognition}
}
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