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Langseth, Helge Autor

Deep Contextual Grid Triplet Network for Context-Aware Recommendation

  • Sofia Aftab
  • Heri Ramampiaro
  • Helge Langseth
  • Massimiliano Ruocco

IEEE Access - 1/1/2023

10.1109/access.2023.3310470

Número de citas:

Frontiers in Artificial Intelligence and Applications: Preface

  • Kofod-Petersen A.
  • Heintz F.
  • Langseth H.

Frontiers in Artificial Intelligence and Applications - 1/1/2011

Número de citas:
  • Cuartil SJR: Q3
  • CiteScore: 0,6 (2020)
  • SJR: 0,246 (2011)
  • SNIP: 0,583 (2011)
  • Categorías SJR: Artificial Intelligence (Q3)
Open Access

Probabilistic Models with Deep Neural Networks

  • Andrés R. Masegosa
  • Rafael Cabañas De Paz
  • Helge Langseth
  • Thomas Dyhre Nielsen
  • Antonio Salmerón Cerdán

Entropy - 1/01/2021

10.3390/e23010117

Número de citas: 4 (Web of Science)
  • Cuartil JCR: Q2 (2021)
  • Factor Impacto JCR: 2,738 (2022)
  • Impacto Normalizado por Categoría: 0,156 (2021)
  • CiteScore: 4 (2020)
  • SJR: 0,468 (2020)
  • SNIP: 1,059 (2020)
  • Impacto JCR a 5 años: 2,642
  • Categorías JCR: PHYSICS, MULTIDISCIPLINARY

Scalable learning of probabilistic latent models for collaborative filtering

  • Langseth H.
  • Nielsen T.

Decision Support Systems - 1/6/2015

10.1016/j.dss.2015.03.006

Número de citas: 16 (Web of Science) 15 (Scopus)
  • Cuartil SJR: Q1
  • Factor Impacto JCR: 2,604 (2015)
  • CiteScore: 10,5 (2020)
  • SJR: 1,87 (2015)
  • SNIP: 2,284 (2015)
  • Impacto JCR a 5 años: 3,271
  • Categorías JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
  • Categorías SJR: Arts and Humanities (miscellaneous) (Q1); Developmental and Educational Psychology (Q1); Information Systems (Q1); Information Systems and Management (Q1); Management Information Systems (Q1)
Open Access

What predicts citation counts and translational impact in headache research? A machine learning analysis

  • Danelakis A.
  • Langseth H.
  • Nachev P.
  • Nelson A.
  • Bjørk M.H.
  • Matharu M.S.
  • Tronvik E.
  • May A.
  • Stubberud A.
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Cephalalgia : an international journal of headache - 1/5/2024

10.1177/03331024241251488

Número de citas:
Open Access

Consumer-side fairness in recommender systems: a systematic survey of methods and evaluation

  • Vassøy B.
  • Langseth H.

Artificial Intelligence Review - 1/4/2024

10.1007/s10462-023-10663-5

Número de citas:
  • CiteScore: 23 (2022)
  • SJR: 2,49 (2022)
  • SNIP: 4,347 (2022)

The SACSO methodology for troubleshooting complex systems

  • Jensen F.
  • Kjærulff U.
  • Kristiansen B.
  • Langseth H.
  • Skaanning C.
  • Vomlel J.
  • Vomlelová M.
... Ver más Contraer

Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM - 1/9/2001

10.1017/s0890060401154065

Número de citas: 55 (Scopus)
  • Cuartil SJR: Q1
  • CiteScore: 2,9 (2020)
  • SJR: 0,365 (2001)
  • SNIP: 0,885 (2001)
  • Categorías SJR: Industrial and Manufacturing Engineering (Q1); Artificial Intelligence (Q3)

Application of data-driven models in the analysis of marine power systems

  • Swider A.
  • Langseth H.
  • Pedersen E.

Applied Ocean Research - 1/11/2019

10.1016/j.apor.2019.101934

Número de citas: 4 (Web of Science) 3 (Scopus)
  • Cuartil SJR: Q1
  • Factor Impacto JCR: 2,753 (2019)
  • CiteScore: 4,5 (2020)
  • SJR: 1,288 (2019)
  • SNIP: 2,113 (2019)
  • Impacto JCR a 5 años: 2,935
  • Categorías JCR: ENGINEERING, OCEAN
  • Categorías SJR: Ocean Engineering (Q1)

Effective hate-speech detection in Twitter data using recurrent neural networks

  • Pitsilis G.
  • Ramampiaro H.
  • Langseth H.

APPLIED INTELLIGENCE - 1/12/2018

10.1007/s10489-018-1242-y

Número de citas: 67 (Scopus)
  • Cuartil SJR: Q2
  • Factor Impacto JCR: 2,882 (2018)
  • CiteScore: 6,8 (2020)
  • SJR: 0,651 (2018)
  • SNIP: 1,585 (2018)
  • Impacto JCR a 5 años: 2,712
  • Categorías JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
  • Categorías SJR: Artificial Intelligence (Q2)

Parameter learning in object-oriented Bayesian networks

  • Langseth H.
  • Bangsø O.

Annals of Mathematics and Artificial Intelligence - 1/12/2001

10.1023/a:1016769618900

Número de citas: 27 (Scopus)
  • Cuartil SJR: Q2
  • CiteScore: 2,5 (2020)
  • SJR: 0,747 (2001)
  • SNIP: 1,542 (2001)
  • Categorías SJR: Applied Mathematics (Q2); Artificial Intelligence (Q2)

Inference in hybrid Bayesian networks with Mixtures of Truncated Basis Functions

  • Langseth H.
  • Nielsen T.
  • Rumí R.
  • Salmerón A.

Proceedings of the Sixth European Workshop on Probabilistic Graphical Models - 1/12/2012

Número de citas: 11 (Scopus)
  • Categorías SJR: Statistics and Probability

Applications of Bayesian networks in reliability analysis

  • Langseth H.
  • Portinale L.

Bayesian Network Technologies: Applications and Graphical Models - 1/12/2007

10.4018/978-1-59904-141-4.ch005

Número de citas: 7 (Scopus)

Bayesian networks in reliability: The good, the bad, and the ugly

  • Langseth H.

Advances in Mathematical Modeling for Reliability - 1/5/2008

Número de citas: 14 (Scopus)

Learning hybrid bayesian networks using mixtures of truncated basis functions. Aprendizaje de redes bayesianas híbridas con mixturas de funciones base truncadas

  • Inmaculada Pérez-Bernabé
  • Antonio Salmerón Cerdán
  • Helge Langseth

2015

Número de citas:
  • Dialnet
Open Access

Parameter learning in MTE networks using incomplete data

  • Fernández A.
  • Langseth H.
  • Nielsen T.
  • Salmerón A.

The Fifth European Workshop on Probabilistic Graphical Models, Helsinki, Finland, 12/09/2010 - 1/12/2010

Número de citas: 4 (Scopus)
  • Categorías SJR: Statistics and Probability

Dynamic Bayesian modeling for risk prediction in credit operations

  • Borchani H.
  • Martínez A.
  • Masegosa A.
  • Langseth H.
  • Nielsen T.
  • Salmerón A.
  • Fernández A.
  • Madsen A.
  • Sáez R.
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The 13th Scandinavian Conference on Artificial Intelligence (SCAI'2015) - 1/1/2015

10.3233/978-1-61499-589-0-17

Número de citas: 4 (Web of Science) 4 (Scopus)
  • Cuartil SJR: Q3
  • Impacto Normalizado por Categoría: 0,286 (2015)
  • CiteScore: 0,6 (2020)
  • SJR: 0,238 (2015)
  • SNIP: 0,429 (2015)
  • Categorías SJR: Artificial Intelligence (Q3)
Open Access

MPE inference in conditional linear gaussian networks

  • Salmerón A.
  • Rumí R.
  • Langseth H.
  • Madsen A.
  • Nielsen T.

Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 1/1/2015

10.1007/978-3-319-20807-7_37

Número de citas: 4 (Scopus)
  • CiteScore: 1,8 (2020)
  • SJR: 0,369 (2015)
  • SNIP: 0,708 (2015)

Comparative study of event prediction in power grids using supervised machine learning methods

  • Hoiem K.W.
  • Santi V.
  • Torsater B.N.
  • Langseth H.
  • Andresen C.A.
  • Rosenlund G.H.

SEST 2020 - 3rd International Conference on Smart Energy Systems and Technologies - 1/9/2020

10.1109/sest48500.2020.9203025

Número de citas: 2 (Scopus)

Probability-based approach for predicting e-commerce consumer behaviour using sparse session data

  • Myklatun Ø.
  • Thorrud T.
  • Nguyen H.
  • Langseth H.
  • Kofod-Petersen A.

Proceedings of the International ACM Recommender Systems Challenge 2015 - 16/9/2015

10.1145/2813448.2813514

Número de citas:
  • Categorías SJR: Engineering (miscellaneous)
Open Access

Parameter estimation in mixtures of truncated exponentials

  • Langseth H.
  • Nielsen T.
  • Rumí R.
  • Salmerón A.

Proceedings of the Fourth European Workshop on Probabilistic Graphical Models - 1/12/2008

Número de citas: 3 (Scopus)
  • Categorías SJR: Statistics and Probability
Open Access

Scalable MAP inference in Bayesian networks based on a Map-Reduce approach

  • Ramos-L pez D.o.
  • Salmer n A.
  • Rum R.
  • Mart nez A.M.
  • Nielsen T.D.
  • Masegosa A.R.
  • Langseth H.
  • Madsen A.L.
... Ver más Contraer

Proceedings of the Eighth International Conference on Probabilistic Graphical Models - 1/1/2016

Número de citas:
  • Factor Impacto JCR: 5 (2016)
  • CiteScore: 6,8 (2020)
  • SJR: 1,29 (2016)
  • SNIP: 2,222 (2016)
  • Impacto JCR a 5 años: 7,649
  • Categorías JCR: AUTOMATION & CONTROL SYSTEMS
Open Access

A new method for vertical parallelisation of TAN learning based on balanced incomplete block designs

  • Madsen A.
  • Jensen F.
  • Salmerón A.
  • Karlsen M.
  • Langseth H.
  • Nielsen T.

Proceedings of the 7th European Workshop on Probabilistic Graphical Models - 1/1/2014

10.1007/978-3-319-11433-0_20

Número de citas: 9 (Web of Science) 10 (Scopus)
  • Impacto Normalizado por Categoría: 0,839 (2014)
  • CiteScore: 1,8 (2020)
  • SJR: 0,354 (2014)
  • SNIP: 0,753 (2014)

Prediction intervals: Split normal mixture from quality-driven deep ensembles

  • Salem T.S.
  • Langseth H.
  • Ramampiaro H.

Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, UAI 2020 - 1/1/2020

Número de citas: 1 (Scopus)

Bayesian models of data streams with Hierarchical Power Priors

  • Masegosa A.
  • Nielsen T.
  • Langseth H.
  • Ramos-López D.
  • Salmerón A.
  • Madsen A.

Proceedings of the 34th International Conference on Machine Learning - 1/1/2017

Número de citas: 2 (Scopus)

Este autor no tiene patentes.

Este autor no tiene informes ni otros tipos de publicaciones.

Scopus: 18

Web of Science: 12

Scopus: 33

Web of Science: 13

Última actualización de los datos: 18/05/24 9:32
Próxima recolección programada: 25/05/24 3:00