RDF/XML JSON-LD N3/Turtle RDF/JSON

Langseth, Helge Author

Analysis of OREDA data for maintenance optimisation

  • Langseth H.
  • Haugen K.
  • Sandtorv H.

Reliability Engineering and System Safety - 1/1/1998

10.1016/s0951-8320(98)83003-2

Cite count: 18 (Scopus)
  • CiteScore: 9.3 (2020)

Uncertainty bounds for a monotone multistate system

  • Langseth H.
  • Lindqvist B.

Probability in the Engineering and Informational Sciences - 1/12/1998

10.1017/s0269964800005179

Cite count: 10 (Scopus)
  • CiteScore: 1.7 (2020)

Parameter learning in object-oriented Bayesian networks

  • Langseth H.
  • Bangsø O.

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

10.1023/a:1016769618900

Cite count: 27 (Scopus)
  • SJR Quartile: Q2
  • CiteScore: 2.5 (2020)
  • SJR: 0.747 (2001
  • SNIP: 1.542 (2001
  • SJR Categories: Applied Mathematics (Q2); Artificial Intelligence (Q2)

The SACSO methodology for troubleshooting complex systems

  • Jensen F.
  • Kjærulff U.
  • Kristiansen B.
  • Langseth H.
  • Skaanning C.
  • Vomlel J.
  • Vomlelová M.
... View more Collapse

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

10.1017/s0890060401154065

Cite count: 55 (Scopus)
  • SJR Quartile: Q1
  • CiteScore: 2.9 (2020)
  • SJR: 0.365 (2001
  • SNIP: 0.885 (2001
  • SJR Categories: Industrial and Manufacturing Engineering (Q1); Artificial Intelligence (Q3)

Decision theoretic troubleshooting of coherent systems

  • Langseth H.
  • Jensen F.

Reliability Engineering and System Safety - 1/4/2003

10.1016/s0951-8320(02)00202-8

Cite count: 25 (Scopus)
  • SJR Quartile: Q1
  • CiteScore: 9.3 (2020)
  • SJR: 0.414 (2003
  • SNIP: 1.307 (2003
  • SJR Categories: Industrial and Manufacturing Engineering (Q1); Safety, Risk, Reliability and Quality (Q2); Applied Mathematics (Q3)
Open Access

Fusion of domain knowledge with data for structural learning in object oriented domains

  • Langseth H.
  • Nielsen T.

Journal of Machine Learning Research - 1/4/2004

10.1162/153244304773633852

Cite count: 16 (Web of Science) 32 (Scopus)
  • SJR Quartile: Q1
  • CiteScore: 6.8 (2020)
  • SJR: 1.691 (2004
  • SNIP: 6.419 (2004
  • SJR Categories: Artificial Intelligence (Q1); Control and Systems Engineering (Q1); Software (Q1); Statistics and Probability (Q1)

Failure modeling and maintenance optimization for a railway line

  • Hokstad P.
  • Langseth H.
  • Lindqvist B.
  • Vatn J.

International Journal of Performability Engineering - 1/1/2005

Cite count: 8 (Scopus)
  • CiteScore: 0.8 (2020)
  • SJR Categories: Safety, Risk, Reliability and Quality

Latent classification models

  • Langseth H.
  • Nielsen T.

Machine Learning - 1/6/2005

10.1007/s10994-005-0472-5

Cite count: 10 (Web of Science) 13 (Scopus)
  • SJR Quartile: Q1
  • CiteScore: 6.1 (2020)
  • SJR: 2.604 (2005
  • SNIP: 5.656 (2005
  • SJR Categories: Artificial Intelligence (Q1); Software (Q1)

Competing risks for repairable systems: A data study

  • Langseth H.
  • Lindqvist B.

Journal of Statistical Planning and Inference - 1/5/2006

10.1016/j.jspi.2004.10.032

Cite count: 33 (Scopus)
  • SJR Quartile: Q2
  • CiteScore: 1.4 (2020)
  • SJR: 0.937 (2006
  • SNIP: 1.051 (2006
  • SJR Categories: Applied Mathematics (Q2); Statistics and Probability (Q2); Statistics, Probability and Uncertainty (Q2)

Classification using Hierarchical Naïve Bayes models

  • Langseth H.
  • Nielsen T.

Machine Learning - 1/5/2006

10.1007/s10994-006-6136-2

Cite count: 47 (Web of Science) 62 (Scopus)
  • SJR Quartile: Q1
  • CiteScore: 6.1 (2020)
  • SJR: 1.8 (2006
  • SNIP: 4.144 (2006
  • SJR Categories: Artificial Intelligence (Q1); Software (Q1)

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

Cite count: 7 (Scopus)

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

  • Langseth H.

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

Cite count: 14 (Scopus)

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

Cite count: 11 (Scopus)
  • SJR Categories: Statistics and Probability

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

Cite count:
  • Dialnet

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.
... View more Collapse

Proceedings - International Symposium on Wearable Computers, ISWC - 8/10/2018

10.1145/3267242.3267286

Cite count: 54 (Scopus)
  • SJR Categories: Computer Networks and Communications; Engineering (miscellaneous); Hardware and Architecture; Software

Forecasting intra-hour imbalances in electric power systems

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

33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 - 1/1/2019

Cite count: 4 (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

Cite count: 1 (Scopus)

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

Cite count: 2 (Scopus)

Detection of Potential Manipulations in Electricity Market using Machine Learning Approaches

  • Tiwari, Shweta
  • Bell, Gavin
  • Langseth, Helge
  • Ramampiaro, Heri
  • Rocha, AP
  • Steels, L
  • VandenHerik, J
... View more Collapse

ICAART: PROCEEDINGS OF THE 14TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 3 - 2022

10.5220/0010991800003116

On Reporting Robust and Trustworthy Conclusions from Model Comparison Studies Involving Neural Networks and Randomness

  • Odd Erik Gundersen
  • Saeid Shamsaliei
  • Håkon Sletten Kjærnli
  • Helge Langseth

Proceedings of the 1st ACM Conference on Reproducibility and Replicability, REP 2023 - 27/06/2023

10.1145/3589806.3600044

Cite count:

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

Cite count:
Open Access

Bayesian Exploration in Deep Reinforcement Learning

  • Killingberg L.
  • Langseth H.

CEUR Workshop Proceedings - 1/1/2023

Cite count:
  • CiteScore: 1.1 (2022)
  • SJR: 0.202 (2022
  • SNIP: 0.223 (2022

This author has no patents.

This author has no reports or other types of publications.

Scopus: 18

Web of Science: 12

Scopus: 33

Web of Science: 13

Last data update: 5/18/24 9:32 AM
Next scheduled update: 5/25/24 3:00 AM