Joey Öhman

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Fredrik CarlssonResearcher, Research Institutes of SwedenVerified email at ri.se
Magnus SahlgrenAI SwedenVerified email at ai.se
Severine VerlindenNLP Engineer @ TechwolfVerified email at techwolf.ai
Ariel EkgrenResearch Scientist at AI SwedenVerified email at ai.se
Evangelia GogoulouRISE Research Institute of Technology, KTH Royal Institute of TechnologyVerified email at ri.se
Amaru Cuba GyllenstenPhDVerified email at kth.se
David BromanProfessor, KTH Royal Institute of TechnologyVerified email at kth.se
Daniel LundénOracleVerified email at oracle.com
Fangyu LiuResearch Scientist, Google DeepMindVerified email at google.com
Joakim NivreProfessor of Computational Linguistics, Uppsala University; Senior Researcher, RISEVerified email at lingfil.uu.se
Viktor SenderovSwedish Museum for Natural HistoryVerified email at nrm.se
Jan KudlickaAssociate Professor of Data Science, BI Norwegian Business SchoolVerified email at bi.no
Fredrik RonquistProfessor, Swedish Museum of Natural History, Dept. Bioinformatics and GeneticsVerified email at nrm.se

Joey Öhman

Embark Studios

Verified email at embark-studios.com

Machine Learning Natural Language Understanding Reinforcement Learning


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Fine-grained controllable text generation using non-residual prompting F Carlsson, J Öhman, F Liu, S Verlinden, J Nivre, M Sahlgren Proceedings of the 60th Annual Meeting of the Association for Computational …, 2022	35	2022
Compiling Universal Probabilistic Programming Languages with Efficient Parallel Sequential Monte Carlo Inference. D Lundén, J Öhman, J Kudlicka, V Senderov, F Ronquist, D Broman ESOP, 29-56, 2022	10	2022
Lessons Learned from GPT-SW3: Building the First Large-Scale Generative Language Model for Swedish A Ekgren, AC Gyllensten, E Gogoulou, A Heiman, S Verlinden, J Öhman, ... Proceedings of the Thirteenth Language Resources and Evaluation Conference …, 2022	8	2022
GPT-SW3: An Autoregressive Language Model for the Nordic Languages A Ekgren, AC Gyllensten, F Stollenwerk, J Öhman, T Isbister, E Gogoulou, ... arXiv preprint arXiv:2305.12987, 2023	4	2023
Alphazero to alpha hero: A pre-study on additional tree sampling within self-play reinforcement learning F Carlsson, J Öhman	4	2019
The Nordic Pile: A 1.2 TB Nordic Dataset for Language Modeling J Öhman, S Verlinden, A Ekgren, AC Gyllensten, T Isbister, E Gogoulou, ... arXiv preprint arXiv:2303.17183, 2023	3	2023
Superlim: A Swedish language understanding evaluation benchmark A Berdičevskis, G Bouma, R Kurtz, F Morger, J Öhman, Y Adesam, L Borin, ... Proceedings of the 2023 Conference on Empirical Methods in Natural Language …, 2023	2	2023
Annotated Job Ads with Named Entity Recognition F Stollenwerk, N Fastlund, A Nyqvist, J Öhman arXiv preprint arXiv:2310.11769, 2023	2	2023
DaLAJ-GED-a dataset for Grammatical Error Detection tasks on Swedish E Volodina, YA Mohammed, A Berdičevskis, G Bouma, J Öhman Proceedings of the 12th Workshop on NLP for Computer Assisted Language …, 2023	2	2023
Active learning for named entity recognition with swedish language models J Öhman	2	2021
Text Annotation Handbook: A Practical Guide for Machine Learning Projects F Stollenwerk, J Öhman, D Petrelli, E Wallerö, F Olsson, C Bengtsson, ... arXiv preprint arXiv:2310.11780, 2023		2023
Universal Probabilistic Programming Language Compilation with Parallel Efficient Sequential Monte Carlo Inference D Lundén, J Öhman, J Kudlicka, V Senderov, F Ronquist, D Broman arXiv preprint arXiv:2112.00364, 2021		2021
Compilation of Universal Probabilistic Programs to GPGPUs D Lundén, J Öhman, D Broman

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