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Langseth, Helge Author

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

Cite count:
  • CiteScore: 23 (2022)
  • SJR: 2.49 (2022
  • SNIP: 4.347 (2022

Applying Bayesian optimization to predict parameters in a time-domain model for cross-flow vortex-induced vibrations

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

Marine Structures - 1/3/2024

10.1016/j.marstruc.2023.103571

Cite count:
  • CiteScore: 7.6 (2022)
  • SJR: 1.228 (2022
  • SNIP: 2.239 (2022

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

Cite count:

Machine Learning in Financial Market Surveillance: A Survey

  • Tiwari S.
  • Ramampiaro H.
  • Langseth H.

IEEE Access - 1/1/2021

10.1109/access.2021.3130843

Cite count:
  • JCR Impact Factor: 3.476 (2021)
  • JCR 5-year Impact Factor: 3.758
  • JCR Categories: COMPUTER SCIENCE, INFORMATION SYSTEMS
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

Cite count: 4 (Web of Science)
  • JCR Quartile: Q2 (2021)
  • JCR Impact Factor: 2.738 (2022)
  • Category normalized Impact: 0.156 (2021)
  • CiteScore: 4 (2020)
  • SJR: 0.468 (2020
  • SNIP: 1.059 (2020
  • JCR 5-year Impact Factor: 2.642
  • JCR Categories: PHYSICS, MULTIDISCIPLINARY
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

Cite count: 8 (Web of Science) 2 (Scopus)
  • JCR Quartile: Q1 (2020)
  • JCR Impact Factor: 2.258 (2022)
  • Category normalized Impact: 2.467 (2020)
  • JCR 5-year Impact Factor: 2.165
  • JCR Categories: MATHEMATICS

Analyzing concept drift: A case study in the financial sector

  • Masegosa A.R.
  • Martínez A.M.
  • Ramos-López D.
  • Langseth H.
  • Nielsen T.D.
  • Salmerón A.

Intelligent Data Analysis - 21/05/2020

10.3233/ida-194515

Cite count: 7 (Web of Science) 1 (Scopus)
  • JCR Quartile: Q4 (2020)
  • JCR Impact Factor: 0.86 (2022)
  • Category normalized Impact: 0.379 (2020)
  • CiteScore: 1.7 (2020)
  • SJR: 0.231 (2020
  • SNIP: 0.527 (2020
  • JCR 5-year Impact Factor: 0.894
  • JCR Categories: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

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

Cite count: 4 (Web of Science) 3 (Scopus)
  • SJR Quartile: Q1
  • JCR Impact Factor: 2.753 (2019)
  • CiteScore: 4.5 (2020)
  • SJR: 1.288 (2019
  • SNIP: 2.113 (2019
  • JCR 5-year Impact Factor: 2.935
  • JCR Categories: ENGINEERING, OCEAN
  • SJR Categories: Ocean Engineering (Q1)

Learning similarity measures from data

  • Mathisen B.
  • Aamodt A.
  • Bach K.
  • Langseth H.

Progress in Artificial Intelligence - 1/1/2019

10.1007/s13748-019-00201-2

Cite count: 21 (Web of Science) 15 (Scopus)
  • SJR Quartile: Q2
  • CiteScore: 3.4 (2020)
  • SJR: 0.322 (2020
  • SNIP: 0.746 (2020
  • SJR Categories: Artificial Intelligence (Q2)

AMIDST: A Java toolbox for scalable probabilistic machine learning

  • Masegosa A.
  • Martínez A.
  • Ramos-López D.
  • Cabañas R.
  • Salmerón A.
  • Langseth H.
  • Nielsen T.
  • Madsen A.
... View more Collapse

Knowledge-Based Systems - 1/01/2019

10.1016/j.knosys.2018.09.019

Cite count: 7 (Web of Science) 7 (Scopus)
  • JCR Quartile: Q1 (2019)
  • SJR Quartile: Q1
  • JCR Impact Factor: 5.921 (2022)
  • Category normalized Impact: 0.374 (2019)
  • CiteScore: 11.3 (2020)
  • SJR: 1.754 (2019
  • SNIP: 2.828 (2019
  • JCR 5-year Impact Factor: 6.075
  • JCR Categories: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
  • SJR Categories: Artificial Intelligence (Q1); Information Systems and Management (Q1); Management Information Systems (Q1); Software (Q1)

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

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

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)

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)
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

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:

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:

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

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)

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)

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)

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

Inter-session modeling for session-based recommendation

  • Ruocco M.
  • Skrede O.
  • Langseth H.

ACM International Conference Proceeding Series - 27/8/2017

10.1145/3125486.3125491

Cite count: 25 (Scopus)
  • SJR Categories: Ocean Engineering
Open Access

Data driven case base construction for prediction of success of marine operations

  • Mathisen B.
  • Aamodt A.
  • Langseth H.

CEUR Workshop Proceedings - 1/1/2017

Cite count: 1 (Scopus)
  • CiteScore: 0.8 (2020)
  • SJR: 0.167 (2017
  • SNIP: 0.346 (2017
  • SJR Categories: Computer Science (miscellaneous)

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: 5/4/24 9:22 AM
Next scheduled update: 5/11/24 3:00 AM