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pro vyhledávání: '"Siivola, Eero"'
The increasing availability of structured but high dimensional data has opened new opportunities for optimization. One emerging and promising avenue is the exploration of unsupervised methods for projecting structured high dimensional data into low d
Externí odkaz:
http://arxiv.org/abs/2012.15471
Autor:
Siivola, Eero, Dhaka, Akash Kumar, Andersen, Michael Riis, Gonzalez, Javier, Moreno, Pablo Garcia, Vehtari, Aki
Most research in Bayesian optimization (BO) has focused on \emph{direct feedback} scenarios, where one has access to exact values of some expensive-to-evaluate objective. This direction has been mainly driven by the use of BO in machine learning hype
Externí odkaz:
http://arxiv.org/abs/2003.11435
Machine learning can help personalized decision support by learning models to predict individual treatment effects (ITE). This work studies the reliability of prediction-based decision-making in a task of deciding which action $a$ to take for a targe
Externí odkaz:
http://arxiv.org/abs/1904.05268
Akademický článek
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Publikováno v:
2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)
Bayesian optimization (BO) is a global optimization strategy designed to find the minimum of an expensive black-box function, typically defined on a compact subset of $\mathcal{R}^d$, by using a Gaussian process (GP) as a surrogate model for the obje
Externí odkaz:
http://arxiv.org/abs/1704.00963
We propose a new method for automatically detecting monotonic input-output relationships from data using Gaussian Process (GP) models with virtual derivative observations. Our results on synthetic and real datasets show that the proposed method detec
Externí odkaz:
http://arxiv.org/abs/1610.05440
The emergence of the Industrial Internet results in an increasing number of complicated temporal interdependencies between automation systems and the processes to be controlled. There is a need for verification methods that scale better than formal v
Externí odkaz:
http://arxiv.org/abs/1602.02504
Autor:
Berg, Annukka, Dahlbo, Helena, Eilu, Pasi, Heikkilä, Pirjo, Hentunen, Ari, Hilska-Keinänen , Katja, Horn, Susanna, Ilvesniemi, Hannu, Jenu, Samppa, Karhu, Marjaana, Karppinen, Tiina K.M., Kauppi, Sari, Kivikytö-Reponen, Päivi, Lavikko, Sonja, Lehtonen, Eeva, Luostarinen, Sari, Majaniemi, Sami, Malmi, Päivi, Naumanen, Mika, Ovaska, Jukka-Pekka, Pesonen, Liisa, Pesu, Janne, Pitkänen, Henri, Pokki, Jussi, Räisänen, Milja, Salminen, Jani, Sapon, Svetlana, Siivola, Eero, Sorvari, Jaana, Tanner, Hannu, Tuovila, Hannamaija, Uusitalo, Teuvo, Valtanen, Kristiina
Publikováno v:
Kauppila, T (ed.), Berg, A, Dahlbo, H, Eilu, P, Heikkilä, P, Hentunen, A, Hilska-Keinänen, K, Horn, S, Ilvesniemi, H, Jenu, S, Karhu, M, Karppinen, T K M, Kauppi, S, Kivikytö-Reponen, P, Lavikko, S, Lehtonen, E, Luostarinen, S, Majaniemi, S, Malmi, P, Naumanen, M, Ovaska, J-P, Pesonen, L, Pesu, J, Pitkänen, H, Pokki, J, Räisänen, M, Salminen, J, Sapon, S, Siivola, E, Sorvari, J, Tanner, H, Tuovila, H, Uusitalo, T & Valtanen, K 2022, Handbook for a Data-Driven Circular Economy in Finland : Data Sources, Tools, and Governance for Circular Design . VTT Technology, no. 401, VTT Technical Research Centre of Finland . https://doi.org/10.32040/2242-122X.2022.T401
The use of raw materials is undergoing a major transformation as living standards rise globally and societies move towards digitalisation and renewable energy production. At the same time, there is a growing awareness of the limits of the carrying ca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=355e65625b88::f04f3975cd48fdc05859ffe2b9e5c6ba
https://cris.vtt.fi/en/publications/8692fa97-9aae-4ff7-9243-0bd2e61569e2
https://cris.vtt.fi/en/publications/8692fa97-9aae-4ff7-9243-0bd2e61569e2
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
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K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
Applied AI Letters; Jun2021, Vol. 2 Issue 2, p1-13, 13p
