Norwegian version
Hugo Lewi Hammer

Hugo Lewi Hammer

Fields of study

Academic disciplines

Statistics

Subject areas

Bayesian statistics   Regression   Artificial intelligence   Machine learning   Applied statistics   Stochastic simulation   Big Data

Countries

Norway

Scientific publications

Thambawita, Vajira; Jha, Debesh; Hammer, Hugo Lewi; Johansen, Håvard D.; Johansen, Dag; Halvorsen, Pål; Riegler, Michael (2020). An Extensive Study on Cross-Dataset Bias and Evaluation Metrics Interpretation for Machine Learning Applied to Gastrointestinal Tract Abnormality Classification. 29 p. ACM Transactions on Computing for Healthcare. Vol. 1.

Abolpour Mofrad, Asieh; Yazidi, Anis; Hammer, Hugo Lewi; Arntzen, Erik (2020). Equivalence Projective Simulation as a Framework for Modeling Formation of Stimulus Equivalence Classes. Neural Computation . Vol. 32.
https://hdl.handle.net/11250/2680093

Borgli, Hanna; Thambawita, Vajira; Smedsrud, Pia H; Hicks, Steven Alexander; Jha, Debesh; Eskeland, Sigrun Losada; Randel, Kristin Ranheim; Pogorelov, Konstantin; Lux, Mathias; Dang Nguyen, Duc Tien; Johansen, Dag; Griwodz, Carsten; Stensland, Håkon Kvale; Garcia-Ceja, Enrique; Schmidt, Peter T; Hammer, Hugo Lewi; Riegler, Michael; Halvorsen, Pål; de Lange, Thomas (2020). HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy. Scientific Data .

Pinto-Orellana, Marco Antonio; Hammer, Hugo Lewi (2020). Dyadic Aggregated Autoregressive Model (DASAR) for Automatic Modulation Classification. IEEE Access .

Pinto-Orellana, Marco Antonio; Hammer, Hugo Lewi (2020). Analysis of Optical Brain Signals Using Connectivity Graph Networks. Lecture Notes in Computer Science (LNCS) .

Yazidi, Anis; Abolpour Mofrad, Asieh; Goodwin, Morten; Hammer, Hugo Lewi; Arntzen, Erik (2020). Balanced difficulty task finder: an adaptive recommendation method for learning tasks based on the concept of state of flow. Cognitive Neurodynamics . Vol. 14.
https://hdl.handle.net/11250/2676071

Yazidi, Anis; Hassan, Ismail; Hammer, Hugo Lewi; Oommen, John (2020). Achieving Fair Load Balancing by Invoking a Learning Automata-based Two Time Scale Separation Paradigm. 14 p. IEEE Transactions on Neural Networks and Learning Systems .

Thambawita, Vajira; Hicks, Steven; Borgli, Hanna; Stensland, Håkon Kvale; Jha, Debesh; Svensen, Martin Kristoffer; Pettersen, Svein Arne; Johansen, Dag; Johansen, Håvard D.; Pettersen, Susann Dahl; Nordvang, Simon; Pedersen, Sigurd; Gjerdrum, Anders Tungeland; Grønli, Tor-Morten; Fredriksen, Per Morten; Eg, Ragnhild; Hansen, Kjeld S.; Fagernes, Siri; Claudi, Christine; Biørn-Hansen, Andreas; Dang Nguyen, Duc Tien; Kupka, Tomas; Hammer, Hugo Lewi; Jain, Ramesh; Riegler, Michael; Halvorsen, Pål (2020). PMData: a sports logging dataset. Alay, Özgü; Toni, Laura (Ed.). MMSys '20: Proceedings of the 11th ACM Multimedia Systems Conference. konferanseartikkel. p. 231-236. Association for Computing Machinery (ACM).

Svoren, Henrik; Thambawita, Vajira; Halvorsen, Pål; Jakobsen, Petter; Garcia-Ceja, Enrique; Noori, Farzan Majeed; Hammer, Hugo Lewi; Lux, Mathias; Riegler, Michael; Hicks, Steven Alexander (2020). Toadstool: a dataset for training emotional intelligent machines playing Super Mario Bros. Alay, Özgü; Toni, Laura (Ed.). MMSys '20: Proceedings of the 11th ACM Multimedia Systems Conference. Article. p. 309-314. Association for Computing Machinery (ACM).

Thambawita, Vajira; Hicks, Steven Alexander; Borgli, Hanna; Stensland, Håkon Kvale; Jha, Debesh; Svensen, Martin Kristoffer; Pettersen, Svein-Arne; Johansen, Dag; Johansen, Håvard Dagenborg; Pettersen, Susann Dahl; Nordvang, Simon; Pedersen, Sigurd; Gjerdrum, Anders Tungeland; Grønli, Tor-Morten; Fredriksen, Per Morten; Eg, Ragnhild; Hansen, Kjeld S.; Fagernes, Siri; Claudi, Christine; Biørn-Hansen, Andreas; Nguyen, Duc Tien Dang; Kupka, Tomas; Hammer, Hugo Lewi; Jain, Ramesh; Riegler, Michael; Halvorsen, Pål (2020). PMData: a sports logging dataset. ISBN: 9781450368452. 387 p. ACM Digital Library.





These publications are obtained from Cristin. The list may be incomplete