Zobrazeno 1 - 10
of 635
pro vyhledávání: '"Categorical distribution"'
Autor:
Tuckerman, Mark E., author
Publikováno v:
Statistical Mechanics: Theory and Molecular Simulation, 2023, ill.
Externí odkaz:
https://doi.org/10.1093/oso/9780198825562.003.0017
Publikováno v:
IEEE Access, Vol 11, Pp 57117-57136 (2023)
Most learning-based methods require labelling the training data, which is time-consuming and gives rise to wrong labels. To address the labelling issues thoroughly, we propose an unsupervised learning framework to remove mismatches by maximizing the
Externí odkaz:
https://doaj.org/article/fac627cc3ef54c7cb76bc4466e4de9be
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(2):9729603, 1353-1371. IEEE Computer Society
The Gumbel-max trick is a method to draw a sample from a categorical distribution, given by its unnormalized (log-)probabilities. Over the past years, the machine learning community has proposed several extensions of this trick to facilitate, e.g., d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1e57d80e0c33fbad4e44a5dae27024ca
https://doi.org/10.1109/tpami.2022.3157042
https://doi.org/10.1109/tpami.2022.3157042
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 33:3216-3226
This article focuses on the sampled-data synchronization issue for a class of complex dynamical networks (CDNs) subject to noisy sampling intervals and successive packet losses. The sampling intervals are subject to noisy perturbations, and categoric
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6d3a97def634e1485cc49f81d23f626b
https://doi.org/10.1109/tnnls.2021.3051052
https://doi.org/10.1109/tnnls.2021.3051052
Autor:
Pesaran, M. H., Yang, L.
This paper considers a first-order autoregressive panel data model with individual specific effects and a heterogeneous autoregressive coefficient. It proposes estimators for the moments of the cross-sectional distribution of the autoregressive coeff
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::02e7b71c9645810488e891cfb3b7563e
Akademický článek
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Autor:
Jiří Dvořák, Tomáš Mrkvička
Publikováno v:
Computational Statistics. 37:671-699
We propose two model-free, permutation-based tests of independence between a pair of random variables. The tests can be applied to samples from any bivariate distribution: continuous, discrete, or mixture of those, with light tails or heavy tails. Ap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::96c1df77f242362934abf14fcef9ced6
https://doi.org/10.1007/s00180-021-01134-y
https://doi.org/10.1007/s00180-021-01134-y
Autor:
Jing Wang
Publikováno v:
Journal of Data Science. 8:43-59
In this paper, we propose a nonparametric approach using the Dirichlet processes (DP) as a class of prior distributions for the distribution G of the random effects in the hierarchical generalized linear mixed model (GLMM). The support of the prior d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b22a4dc2e18e142aec7993ec7066f1b5
https://doi.org/10.6339/jds.2010.08(1).562
https://doi.org/10.6339/jds.2010.08(1).562
Akademický článek
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Autor:
Marwa Moustafa, Hisham M. Abdelsalam, Amira S. Mahmoud, Sayed A. Mohamed, Reda A. El-Khorib, Ihab A. El-Khodary
Publikováno v:
IEEE Access, Vol 9, Pp 90366-90378 (2021)
Change Detection (CD) is a hot remote sensing topic where the change zones are highlighted by analyzing bi-temporal or multi-temporal images. Recently, Deep learning (DL) paved the road to implement various reliable change detection approaches that o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8e1254ccb416ea66ba13bf6c15b473cc
https://ieeexplore.ieee.org/document/9456864/
https://ieeexplore.ieee.org/document/9456864/