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Langseth, Helge Autor

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.
... Ver más Contraer

Cephalalgia - 1/5/2024

10.1177/03331024241251488

Número de citas: 3 (Scopus)
  • CiteScore: 9,4 (2024)
  • SJR: 1,367 (2024)
  • SNIP: 1,41 (2024)
Open Access

Variational Inference over Nonstationary Data Streams for Exponential Family Models †

  • Andrés R. Masegosa
  • Darío Ramos-López
  • Antonio Salmerón
  • Helge Langseth
  • Thomas D. Nielsen

MATHEMATICS - 3/11/2020

10.3390/math8111942

Número de citas: 10 (Web of Science) 11 (Scopus)
  • Cuartil JCR: Q1 (2020)
  • Factor Impacto JCR: 2,258 (2023)
  • Impacto Normalizado por Categoría: 1,811 (2020)
  • CiteScore: 4,6 (2024)
  • SJR: 0,495 (2020)
  • SNIP: 1,258 (2020)
  • Impacto JCR a 5 años: 2,165
  • Categorías JCR: MATHEMATICS

Uncertainty bounds for a monotone multistate system

  • Langseth H.
  • Lindqvist B.

Probability in the Engineering and Informational Sciences - 1/1/1998

10.1017/s0269964800005179

Número de citas: 10 (Scopus)
  • CiteScore: 2,4 (2024)

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: 61 (Scopus)
  • Cuartil SJR: Q1
  • CiteScore: 4,9 (2024)
  • SJR: 0,365 (2001)
  • SNIP: 0,885 (2001)
  • Categorías SJR: Industrial and Manufacturing Engineering (Q1); Artificial Intelligence (Q3)

The Multiquadric Kernel for Moment-Matching Distributional Reinforcement Learning

  • Killingberg L.
  • Langseth H.

Transactions on Machine Learning Research - 1/8/2023

Número de citas: 2 (Scopus)
Open Access

Simulating Vortex-Induced Vibrations in Sheared Current by Using an Empirical Time-Domain Model with Adaptive Parameters

  • Andersen M.L.
  • Sævik S.
  • Wu J.
  • Leira B.J.
  • Langseth H.

Applied Ocean Research - 1/12/2024

10.1016/j.apor.2024.104284

Número de citas: 0 (Scopus)
  • CiteScore: 8,9 (2024)
  • SJR: 1,248 (2024)
  • SNIP: 1,79 (2024)

Short-Term Load Forecasting With Seasonal Decomposition Using Evolution for Parameter Tuning

  • Hoverstad B.
  • Tidemann A.
  • Langseth H.
  • Ozturk P.

IEEE Transactions on Smart Grid - 1/7/2015

10.1109/tsg.2015.2395822

Número de citas: 73 (Scopus)
  • Cuartil SJR: Q1
  • Factor Impacto JCR: 3,19 (2015)
  • CiteScore: 21,1 (2024)
  • SJR: 3,424 (2015)
  • SNIP: 3,281 (2015)
  • Impacto JCR a 5 años: 5,104
  • Categorías JCR: ENGINEERING, ELECTRICAL & ELECTRONIC
  • Categorías SJR: Computer Science (miscellaneous) (Q1)
Open Access

Scaling up Bayesian variational inference using distributed computing clusters

  • Masegosa A.
  • Martinez A.
  • Langseth H.
  • Nielsen T.
  • Salmerón A.
  • Ramos-López D.
  • Madsen A.
... Ver más Contraer

INTERNATIONAL JOURNAL OF APPROXIMATE REASONING - 1/9/2017

10.1016/j.ijar.2017.06.010

Número de citas: 11 (Web of Science) 12 (Scopus)
  • Cuartil JCR: Q2 (2017)
  • Cuartil SJR: Q1
  • Factor Impacto JCR: 1,766 (2023)
  • Impacto Normalizado por Categoría: 0,299 (2017)
  • CiteScore: 6,2 (2024)
  • SJR: 0,866 (2017)
  • SNIP: 1,786 (2017)
  • Impacto JCR a 5 años: 2,504
  • Categorías JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
  • Categorías SJR: Artificial Intelligence (Q1); Software (Q1); Theoretical Computer Science (Q1); Applied Mathematics (Q2)

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) 22 (Scopus)
  • Cuartil SJR: Q1
  • Factor Impacto JCR: 2,604 (2015)
  • CiteScore: 13,5 (2024)
  • 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

Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks

  • Ramos-López D.
  • Masegosa A.
  • Salmerón A.
  • Rumí R.
  • Langseth H.
  • Nielsen T.
  • Madsen A.
... Ver más Contraer

INTERNATIONAL JOURNAL OF APPROXIMATE REASONING - 1/9/2018

10.1016/j.ijar.2018.06.004

Número de citas: 10 (Web of Science) 13 (Scopus)
  • Cuartil JCR: Q2 (2018)
  • Cuartil SJR: Q2
  • Factor Impacto JCR: 1,982 (2023)
  • Impacto Normalizado por Categoría: 0,344 (2018)
  • CiteScore: 6,2 (2024)
  • SJR: 0,606 (2018)
  • SNIP: 1,44 (2018)
  • Impacto JCR a 5 años: 2,2
  • Categorías JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
  • Categorías SJR: Applied Mathematics (Q2); Artificial Intelligence (Q2); Software (Q2); Theoretical Computer Science (Q2)

STATISTICAL MODELING AND INFERENCE FOR COMPONENT FAILURE TIMES UNDER PREVENTIVE MAINTENANCE AND INDEPENDENT CENSORING

  • Lindqvist B.H.
  • Langseth H.

Series on Quality Reliability and Engineering Statistics Vol 10 Modern Statistical and Mathematical Methods in Reliability - 1/1/2005

10.1142/9789812703378_0023

Número de citas: 3 (Scopus)

New Ideas in Ranking for Personalized Fashion Recommender Systems

  • Ramampiaro H.
  • Langseth H.
  • Almenningen T.
  • Schistad H.
  • Havig M.
  • Nguyen H.T.

Business and Consumer Analytics New Ideas - 1/1/2019

10.1007/978-3-030-06222-4_25

Número de citas: 2 (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: 0 (Dialnet)
  • Dialnet

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)

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: 8 (Scopus)

Understanding and improving recurrent networks for human activity recognition by continuous attention

  • Zeng M.
  • Gao H.
  • Yu T.
  • Mengshoel O.
  • Langseth H.
  • Lane I.
  • Liu X.
... Ver más Contraer

Proceedings International Symposium on Wearable Computers Iswc - 8/10/2018

10.1145/3267242.3267286

Número de citas: 158 (Scopus)
  • SJR: 0,359 (2018)
  • SNIP: 2,356 (2018)
  • Categorías SJR: Computer Networks and Communications; Engineering (miscellaneous); Hardware and Architecture; Software

Towards a more expressive model for dynamic classification

  • Zhong S.
  • Martínez A.
  • Nielsen T.
  • Langseth H.

Proceedings of the 23rd International Florida Artificial Intelligence Research Society Conference Flairs 23 - 19/10/2010

Número de citas: 3 (Scopus)
  • Categorías SJR: Artificial Intelligence; Control and Systems Engineering

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

Journal of Machine Learning Research - 1/1/2016

Número de citas: 0 (Scopus)
  • Factor Impacto JCR: 5 (2016)
  • CiteScore: 16,3 (2024)
  • SJR: 1,29 (2016)
  • SNIP: 2,222 (2016)
  • Impacto JCR a 5 años: 7,649
  • Categorías JCR: AUTOMATION & CONTROL SYSTEMS
Open Access

Providing Previously Unseen Users Fair Recommendations Using Variational Autoencoders

  • Bjørnar Vassøy
  • Helge Langseth
  • Benjamin Kille

PROCEEDINGS OF THE 17TH ACM CONFERENCE ON RECOMMENDER SYSTEMS, RECSYS 2023 - 14/09/2023

10.1145/3604915.3608842

Número de citas: 4 (Web of Science) 4 (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: 0 (Scopus)
  • Categorías SJR: Engineering (miscellaneous)

Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles

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

Proceedings of Machine Learning Research - 1/1/2020

Número de citas: 12 (Scopus)

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: 14 (Scopus)

Parameter learning in MTE networks using incomplete data

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

Proceedings of the 5th European Workshop on Probabilistic Graphical Models Pgm 2010 - 1/12/2010

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

Parameter estimation in mixtures of truncated exponentials

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

Proceedings of the 4th European Workshop on Probabilistic Graphical Models Pgm 2008 - 1/12/2008

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

Parallelisation of the PC Algorithm

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

ADVANCES IN ARTIFICIAL INTELLIGENCE (CAEPIA 2015) - 1/1/2015

10.1007/978-3-319-24598-0_2

Número de citas: 7 (Web of Science) 8 (Scopus)
  • Impacto Normalizado por Categoría: 0,64 (2015)
  • CiteScore: 2,4 (2024)
  • SJR: 0,369 (2015)
  • SNIP: 0,72 (2015)

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Scopus: 21

Web of Science: 12

Scopus: 44

Web of Science: 19

Última actualización de los datos: 7/06/25 13:31
Próxima recolección programada: 14/06/25 3:00