Zobrazeno 1 - 10
of 260
pro vyhledávání: '"Joachim Giesen"'
Publikováno v:
Visual Informatics, Vol 8, Iss 2, Pp 67-79 (2024)
Gaussian mixture models are classical but still popular machine learning models. An appealing feature of Gaussian mixture models is their tractability, that is, they can be learned efficiently and exactly from data, and also support efficient exact i
Externí odkaz:
https://doaj.org/article/e2574e3dc054438a8f44b06f8f44a8da
Publikováno v:
Visual Informatics, Vol 7, Iss 2, Pp 72-84 (2023)
Probabilistic programming is a powerful means for formally specifying machine learning models. The inference engine of a probabilistic programming environment can be used for serving complex queries on these models. Most of the current research in pr
Externí odkaz:
https://doaj.org/article/9faa0317750c4b7e8d548bc0fd2542b7
Identifying the skeptics and the undecided through visual cluster analysis of local network geometry
Publikováno v:
Visual Informatics, Vol 6, Iss 3, Pp 11-22 (2022)
By skeptics and undecided we refer to nodes in clustered social networks that cannot be assigned easily to any of the clusters. Such nodes are typically found either at the interface between clusters (the undecided) or at their boundaries (the skepti
Externí odkaz:
https://doaj.org/article/28aa083bd5404fa0b38fc4b78396cfe5
Publikováno v:
Software: Practice and Experience. 52:2684-2699
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9d85eff1cf6170b94f304388af711382
https://doi.org/10.1002/spe.3142
https://doi.org/10.1002/spe.3142
Autor:
Kai Lawonn, Monique Meuschke, Pepe Eulzer, Matthias Mitterreiter, Joachim Giesen, Tobias Gunther
Publikováno v:
IEEE transactions on visualization and computer graphics.
The Gaussian mixture model (GMM) describes the distribution of random variables from several different populations. GMMs have widespread applications in probability theory, statistics, machine learning for unsupervised cluster analysis and topic mode
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::65661f437198bb23e83f1314e34738e7
https://pubmed.ncbi.nlm.nih.gov/36155437
https://pubmed.ncbi.nlm.nih.gov/36155437
Publikováno v:
AAAI
Computing derivatives of tensor expressions, also known as tensor calculus, is a fundamental task in machine learning. A key concern is the efficiency of evaluating the expressions and their derivatives that hinges on the representation of these expr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1257a0610301c2cb51f81616ba21302f
https://doi.org/10.1609/aaai.v34i04.5881
https://doi.org/10.1609/aaai.v34i04.5881
Publikováno v:
it - Information Technology. 62:169-180
Mathematical optimization is at the algorithmic core of machine learning. Almost any known algorithm for solving mathematical optimization problems has been applied in machine learning and the machine learning community itself is actively designing a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::03ae058f9169d722c52d30341cc4097e
https://doi.org/10.1515/itit-2019-0031
https://doi.org/10.1515/itit-2019-0031
Publikováno v:
Computational Science – ICCS 2022 ISBN: 9783031087530
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::09fc8d17dc81df1cadd3e11cb2d91bae
https://doi.org/10.1007/978-3-031-08754-7_2
https://doi.org/10.1007/978-3-031-08754-7_2
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031215339
GENO (generic optimization) is a domain specific language for mathematical optimization. The GENO software generates a solver from a specification of an optimization problem class. The optimization problems, that is, their objective function and cons
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8646dc4ee3168450eadeba1bfd4970e7
https://doi.org/10.1007/978-3-031-21534-6_12
https://doi.org/10.1007/978-3-031-21534-6_12
Autor:
Christoph Staudt, Paul Kahlmeyer, Sina Zarrieß, Joachim Giesen, Frank Nussbaum, Matthias Mitterreiter, Sören Laue
Publikováno v:
IJCAI
Topic models are characterized by a latent class variable that represents the different topics. Traditionally, their observable variables are modeled as discrete variables like, for instance, in the prototypical latent Dirichlet allocation (LDA) topi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1c82fdc48a01d9008571c4dd0bd7ce3a
https://doi.org/10.24963/ijcai.2021/333
https://doi.org/10.24963/ijcai.2021/333