Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Shinichi Nakajima"'
Autor:
Christopher J. Anders, Pan Kessel, Karl Jansen, Tobias Hartung, Shinichi Nakajima, Kim A. Nicoli, Paolo Stornati, Lena Funcke
Publikováno v:
Physical review letters 126(3), 032001 (1-6) (2021). doi:10.1103/PhysRevLett.126.032001
Physical Review Letters
Physical Review Letters
Physical review letters 126(3), 032001 (2021). doi:10.1103/PhysRevLett.126.032001
In this Letter, we demonstrate that applying deep generative machine learning models for lattice field theory is a promising route for solving problems where Marko
In this Letter, we demonstrate that applying deep generative machine learning models for lattice field theory is a promising route for solving problems where Marko
Autor:
Luiz Alberto Lima, Nico Görnitz, Shinichi Nakajima, Klaus-Robert Müller, Luiz Eduardo Seabra Varella
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 29:2743-2756
Analyzing data with latent spatial and/or temporal structure is a challenge for machine learning. In this paper, we propose a novel nonlinear model for studying data with latent dependence structure. It successfully combines the concepts of Markov ra
Autor:
Klaus-Robert Müller, Kim A. Nicoli, Wojciech Samek, Shinichi Nakajima, Nils Strodthoff, Pan Kessel
We propose a general framework for the estimation of observables with generative neural samplers focusing on modern deep generative neural networks that provide an exact sampling probability. In this framework, we present asymptotically unbiased esti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9d2dbe03bbe27f36a5842a85db5249f3
http://arxiv.org/abs/1910.13496
http://arxiv.org/abs/1910.13496
Autor:
Marley M. B. R. Vellasco, Nico Görnitz, Klaus-Robert Müller, Shinichi Nakajima, Luiz Alberto Lima, Luiz Eduardo Seabra Varella
Publikováno v:
Computers & Geosciences. 106:33-48
This paper addresses the porosity estimation problem from seismic impedance volumes and porosity samples located in a small group of exploratory wells. Regression methods, trained on the impedance as inputs and the porosity as output labels, generall
Publikováno v:
Journal of Applied Solution Chemistry and Modeling. 5:71-75
Ion-pair formation constants ( K MA 0 ) for MA + = MPic + and MCl + in water were determined at 298 K and ionic strength → 0 by extraction experiments of picric acid (HPic) in the presence of M(II) in the water phase into benzene (Bz) and by potent
Publikováno v:
ICCV Workshops
In this paper, we propose an efficient variational Bayesian (VB) solver for a robust variant of low-rank subspace clustering (LRSC). VB learning offers automatic model selection without parameter tuning. However, it is typically performed by local se
Autor:
John P. Cunningham, Florian Wenzel, Shinichi Nakajima, Christoph Lippert, Stephan Mandt, Marius Kloft
Linear Mixed Models (LMMs) are important tools in statistical genetics. When used for feature selection, they allow to find a sparse set of genetic traits that best predict a continuous phenotype of interest, while simultaneously correcting for vario
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3490be52a00f8e0c7526f1d0345cab54
Autor:
Ulf Brefeld, Wojciech Samek, Motoaki Kawanabe, Shinichi Nakajima, Christina Müller, Alexander Binder, Klaus-Robert Müller, Marius Kloft
Publikováno v:
Binder, A, Nakajima, S, Kloft, M, Müller, C, Samek, W, Brefeld, U, Müller, K R & Kawanabe, M 2012, ' Insights from classifying visual concepts with multiple kernel learning ' PLoS ONE, vol 7, no. 8, e38897 . DOI: 10.1371/journal.pone.0038897
PLoS ONE, Vol 7, Iss 8, p e38897 (2012)
Binder, A, Nakajima, S, Kloft, M, Müller, C, Samek, W, Brefeld, U, Müller, K R & Kawanabe, M 2012, ' Insights from classifying visual concepts with multiple kernel learning ', PLoS ONE, vol. 7, no. 8, e38897 . https://doi.org/10.1371/journal.pone.0038897
PLoS ONE
PLoS ONE, Vol 7, Iss 8, p e38897 (2012)
Binder, A, Nakajima, S, Kloft, M, Müller, C, Samek, W, Brefeld, U, Müller, K R & Kawanabe, M 2012, ' Insights from classifying visual concepts with multiple kernel learning ', PLoS ONE, vol. 7, no. 8, e38897 . https://doi.org/10.1371/journal.pone.0038897
PLoS ONE
Combining information from various image features has become a standard technique in concept recognition tasks. However, the optimal way of fusing the resulting kernel functions is usually unknown in practical applications. Multiple kernel learning (
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39b3fbc1df1a54adf1fc62d138d963fb
https://pure.leuphana.de/ws/files/14239300/journal.pone.0038897.PDF
https://pure.leuphana.de/ws/files/14239300/journal.pone.0038897.PDF