Zobrazeno 1 - 10
of 96
pro vyhledávání: '"Kai Puolamäki"'
Publikováno v:
PLoS ONE, Vol 19, Iss 1, p e0297714 (2024)
Manifold visualisation techniques are commonly used to visualise high-dimensional datasets in physical sciences. In this paper, we apply a recently introduced manifold visualisation method, slisemap, on datasets from physics and chemistry. slisemap c
Externí odkaz:
https://doaj.org/article/fa47acdfa030492cbbd70575ca58fc37
Publikováno v:
Frontiers in Computer Science, Vol 5 (2023)
In recent years the use of complex machine learning has increased drastically. These complex black box models trade interpretability for accuracy. The lack of interpretability is troubling for, e.g., socially sensitive, safety-critical, or knowledge
Externí odkaz:
https://doaj.org/article/c6bd5b0a83b648c5937531ce1930b466
Publikováno v:
Journal of Eye Movement Research, Vol 10, Iss 4 (2017)
This paper presents a method for computing the gaze point using camera data captured with a wearable gaze tracking device. The method utilizes a physical model of the human eye, advanced Bayesian computer vision algorithms, and Kalman filtering, resu
Externí odkaz:
https://doaj.org/article/aaa94951c713420eab106bd2b07ae561
Autor:
Lauri Ahonen, Benjamin Cowley, Jari Torniainen, Antti Ukkonen, Arto Vihavainen, Kai Puolamäki
Publikováno v:
PLoS ONE, Vol 11, Iss 7, p e0159178 (2016)
It is known that periods of intense social interaction result in shared patterns in collaborators' physiological signals. However, applied quantitative research on collaboration is hindered due to scarcity of objective metrics of teamwork effectivene
Externí odkaz:
https://doaj.org/article/50881b53642d453494fe5de73055076e
Publikováno v:
PLoS Computational Biology, Vol 2, Iss 2, p e6 (2006)
Given a collection of fossil sites with data about the taxa that occur in each site, the task in biochronology is to find good estimates for the ages or ordering of sites. We describe a full probabilistic model for fossil data. The parameters of the
Externí odkaz:
https://doaj.org/article/10762ae482464b46a6954e912b0a974e
Publikováno v:
Statistical Analysis and Data Mining: The ASA Data Science Journal. 16:162-186
Model selection is one of the most central tasks in supervised learning. Validation set methods are the standard way to accomplish this task: models are trained on training data, and the model with the smallest loss on the validation data is selected
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e979611233f21f00c37034069cf0f42
https://doi.org/10.1002/sam.11606
https://doi.org/10.1002/sam.11606
Publikováno v:
Journal of Safety Research. 82:28-37
Introduction: Finnish companies are legally required to insure their employees against occupational acci-dents. Insurance companies are then required to submit information about occupational accidents to the Finnish Workers' Compensation Center (TVK)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6d842e7bc551d4acf80a900d29b1930f
https://doi.org/10.1016/j.jsr.2022.04.003
https://doi.org/10.1016/j.jsr.2022.04.003
Autor:
Anton Rusanen, Anton Björklund, Manousos Manousakas, Jianhui Jiang, Markku T. Kulmala, Kai Puolamäki, Kaspar R. Daellenbach
The concentrations and sources of particulate matter in the atmosphere are temporally auto-correlated. Here, we present a Bayesian matrix factorization model (BAMF) that considers the temporal auto-correlation of the components (sources) and provides
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e93cdb5ecdd3c9ed4ec97d5867c2c520
https://doi.org/10.5194/amt-2023-70
https://doi.org/10.5194/amt-2023-70
Autor:
Jarmo Mäkelä, Laila Melkas, Ivan Mammarella, Tuomo Nieminen, Suyog Chandramouli, Rafael Savvides, Kai Puolamäki
Publikováno v:
Biogeosciences. 19:2095-2099
In this note, we argue that the outputs of causal discovery algorithms should not usually be considered end results but rather starting points and hypotheses for further study. The incentive to explore this topic came from a recent study by Krich et
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d23bd59a687e1a3e185896f076f5b7a2
https://doi.org/10.5194/bg-19-2095-2022
https://doi.org/10.5194/bg-19-2095-2022
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783031264214
We introduce a Python library, called slisemap, that contains a supervised dimensionality reduction method that can be used for global explanation of black box regression or classification models. slisemap takes a data matrix and predictions from a b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::919d68148d0f426ff910932c9b01c319
http://hdl.handle.net/10138/356701
http://hdl.handle.net/10138/356701