Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Eugenia Koblents"'
Publikováno v:
Complexity, Vol 2019 (2019)
In this paper we investigate Monte Carlo methods for the approximation of the posterior probability distributions in stochastic kinetic models (SKMs). SKMs are multivariate Markov jump processes that model the interactions among species in biological
Externí odkaz:
https://doaj.org/article/dacb1fae80a947f7b448fd6442645f34
Autor:
Eugenia Koblents, Luis Lebron Casas
Publikováno v:
MultiMedia Modeling-25th International Conference, MMM 2019, Thessaloniki, Greece, January 8–11, 2019, Proceedings, Part II
MultiMedia Modeling ISBN: 9783030057152
MMM (2)
Lecture Notes in Computer Science
Lecture Notes in Computer Science-MultiMedia Modeling
MultiMedia Modeling ISBN: 9783030057152
MMM (2)
Lecture Notes in Computer Science
Lecture Notes in Computer Science-MultiMedia Modeling
In this paper we propose two video summarization models based on the recently proposed vsLSTM and dppLSTM deep networks, which allow to model frame relevance and similarity. The proposed deep learning architectures additionally incorporate an attenti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c2b8f8e5c1e6ed761eac5e8234af6cdb
Autor:
Federico Alvarez, Eugenia Koblents
Publikováno v:
Proceedings of 8th International Conference on Imaging for Crime Detection and Prevention (ICDP 2017) | 8th. International Conference on Imaging for Crime Detection and Prevention Imaging for Crime Detection and Prevention, 2017 | 13/12/2017-15/12/2017 | Madrid, Spain
Archivo Digital UPM
Universidad Politécnica de Madrid
ICDP
Archivo Digital UPM
Universidad Politécnica de Madrid
ICDP
In this article we propose a novel approach for evidence recommendation in digital forensic applications. The so-called Evidence Graph (EG) is a Heterogeneous Information Network (HIN) constructed from entities and relationships extracted from large
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0da49d48d4054a81cf5e39b9077cbea3
https://oa.upm.es/50820/
https://oa.upm.es/50820/
Autor:
Ernesto La Mattina, Apostolos Axenopoulos, Petros Daras, Antonio Penta, Volker Eiselein, Eugenia Koblents
Digital forensics departments usually have to analyse vast amounts of audio-visual content, such as videos collected from street CCTV footage, hard drives or online resources. The framework presented in this article, which has been developed in the c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b979d0cd11e482fb1af5bf4ff4d7624b
Publikováno v:
e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
instname
instname
The class of $\alpha$-stable distributions enjoys multiple practical applications in signal processing, finance, biology and other areas because it allows to describe interesting and complex data patterns, such as asymmetry or heavy tails, in contras
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2c20adb05743087f30736c94f10b45e0
https://hdl.handle.net/10016/25855
https://hdl.handle.net/10016/25855
Autor:
Joaquín Míguez, Eugenia Koblents
Publikováno v:
CAMSAP
Particle filters are simulation-based algorithms for computational inference in dynamical systems that have become very popular over the years in many areas of science and engineering. They are derived from Bayes' theorem and the technique of importa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cecf646c0411d136888a84aaf0b16844
https://doi.org/10.1109/camsap.2013.6714083
https://doi.org/10.1109/camsap.2013.6714083
Autor:
Joaquín Míguez, Eugenia Koblents
Publikováno v:
ICASSP
In this paper we address the Monte Carlo approximation of integrals with respect to probability distributions in high-dimensional spaces. In particular, we investigate the population Monte Carlo (PMC) scheme, which is based on an iterative importance
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::de5ebd4264a005f05c94e78dd227d622
https://doi.org/10.1109/icassp.2013.6638881
https://doi.org/10.1109/icassp.2013.6638881
Autor:
Eugenia Koblents, Joaquín Míguez
Publikováno v:
e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
instname
instname
This paper addresses the problem of Monte Carlo approximation of posterior probability distributions. In particular, we have considered a recently proposed technique known as population Monte Carlo (PMC), which is based on an iterative importance sam
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f414182ba503c871dac34842288bb12f
http://arxiv.org/abs/1208.5600
http://arxiv.org/abs/1208.5600