Blar i NTNU Open på forfatter "Hammer, Hugo Lewi"
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Approximate Bayesian Inference Based on Expected Evaluations
Hammer, Hugo Lewi; Riegler, Michael; Tjelmeland, Håkon (Peer reviewed; Journal article, 2023)Approximate Bayesian computing (ABC) and Bayesian Synthetic likelihood (BSL) are two popular families of methods to evaluate the posterior distribution when the likelihood function is not available or tractable. For existing ... -
Artificial intelligence in the fertility clinic: status, pitfalls and possibilities
Riegler, Michael Alexander; Stensen, Mette Haug; Witczak, Oliwia; Andersen, Jorunn Marie; Hicks, Steven; Hammer, Hugo Lewi; Delbarre, Erwan; Halvorsen, Pål; Yazidi, Anis; Holst, Nicolai; Haugen, Trine B. (Journal article; Peer reviewed, 2021)In recent years, the amount of data produced in the field of ART has increased exponentially. The diversity of data is large, ranging from videos to tabular data. At the same time, artificial intelligence (AI) is progressively ... -
Associations Between Immigration-Related User Factors and eHealth Activities for Self-Care: Case of First-Generation Immigrants From Pakistan in the Oslo Area, Norway
Tatara, Naoe; Hammer, Hugo Lewi; Mirkovic, Jelena; Kjøllesdal, Marte Karoline Råberg; Andreassen, Hege Kristin (Journal article; Peer reviewed, 2019)Background: Immigrant populations are often disproportionally affected by chronic diseases, such as type 2 diabetes mellitus (T2DM). Use of information and communication technology (ICT) is one promising approach for better ... -
Balanced difficulty task finder: an adaptive recommendation method for learning tasks based on the concept of state of flow
Yazidi, Anis; Abolpour Mofrad, Asieh; Goodwin, Morten; Hammer, Hugo Lewi; Arntzen, Erik (Peer reviewed; Journal article, 2020)An adaptive task difficulty assignment method which we reckon as balanced difficulty task finder (BDTF) is proposed in this paper. The aim is to recommend tasks to a learner using a trade-off between skills of the learner ... -
Bias og kvantitativ analyse innen velferd: opphav til skjevheter og relasjon til utfallsrettferdighet
Storås, Andrea; Prabhu, Robindra; Hammer, Hugo Lewi; Strumke, Inga (Peer reviewed; Journal article, 2022)Ifølge Norges nasjonale strategi for kunstig intelligens er offentlig forvaltning og helse blant Norges satsingsområder for bruk av kunstig intelligens. Maskinlæring er en undergruppe av kunstig intelligens med potensial ... -
A Deep Diagnostic Framework Using Explainable Artificial Intelligence and Clustering
Thunold, Håvard Horgen; Riegler, Michael; Yazidi, Anis; Hammer, Hugo Lewi (Journal article; Peer reviewed, 2023)An important part of diagnostics is to gain insight into properties that characterize a disease. Machine learning has been used for this purpose, for instance, to identify biomarkers in genomics. However, when patient data ... -
Enhanced Equivalence Projective Simulation: A Framework for Modeling Formation of Stimulus Equivalence Classes
Abolpour Mofrad, Asieh; Yazidi, Anis; Abolpour Mofrad, Samaneh; Hammer, Hugo Lewi; Arntzen, Erik (Peer reviewed; Journal article, 2021)Formation of stimulus equivalence classes has been recently modeled through equivalence projective simulation (EPS), a modified version of a projective simulation (PS) learning agent. PS is endowed with an episodic memory ... -
Equivalence Projective Simulation as a Framework for Modeling Formation of Stimulus Equivalence Classes
Abolpour Mofrad, Asieh; Yazidi, Anis; Hammer, Hugo Lewi; Arntzen, Erik (Journal article; Peer reviewed, 2020)Stimulus equivalence (SE) and projective simulation (PS) study complex behavior, the former in human subjects and the latter in artificial agents. We apply the PS learning framework for modeling the formation of equivalence ... -
Estimating tukey depth using incremental quantile estimators
Hammer, Hugo Lewi; Yazidi, Anis; Rue, Håvard (Peer reviewed; Journal article, 2022)Measures of distance or how data points are positioned relative to each other are fundamental in pattern recognition. The concept of depth measures how deep an arbitrary point is positioned in a dataset, and is an interesting ... -
EvoDynamic: A Framework for the Evolution of Generally Represented Dynamical Systems and Its Application to Criticality
Pontes-Filho, Sidney; Lind, Pedro; Yazidi, Anis; Zhang, Jianhua; Hammer, Hugo Lewi; Mello, Gustavo; Sandvig, Ioanna; Tufte, Gunnar; Nichele, Stefano (Chapter, 2020)Dynamical systems possess a computational capacity that may be exploited in a reservoir computing paradigm. This paper presents a general representation of dynamical systems which is based on matrix multiplication. That ... -
A general representation of dynamical systems for reservoir computing
Pontes-Filho, Sidney; Yazidi, Anis; Zhang, Jianhua; Hammer, Hugo Lewi; Mello, Gustavo; Sandvig, I.; Tufte, Gunnar; Nichele, Stefano (Chapter, 2019)Dynamical systems are capable of performing computation in a reservoir computing paradigm. This paper presents a general representation of these systems as an artificial neural network (ANN). Initially, we implement the ... -
Lithology and fluid prediction from prestack seismic data using a Bayesian model with Markov process prior
Hammer, Hugo Lewi; Kolbjørnsen, Odd; Tjelmeland, Håkon; Buland, Arild (Journal article; Peer reviewed, 2012) -
A neuro-inspired general framework for the evolution of stochastic dynamical systems: Cellular automata, random Boolean networks and echo state networks towards criticality
Pontes-Filho, Sidney; Lind, Pedro; Yazidi, Anis; Zhang, Jianhua; Hammer, Hugo Lewi; Mello, Gustavo; Sandvig, Ioanna; Tufte, Gunnar; Nichele, Stefano (Peer reviewed; Journal article, 2020)Although deep learning has recently increased in popularity, it suffers from various problems including high computational complexity, energy greedy computation, and lack of scalability, to mention a few. In this paper, ... -
Recovering Request Patterns to a CPU Processor from Observed CPU Consumption Data
Hammer, Hugo Lewi; Yazidi, Anis; Bratterud, Alfred; Haugerud, Hårek; Feng, Boning (Chapter, 2016)Statistical queuing models are popular to analyze a computer systems ability to process different types requests. A common strategy is to run stress tests by sending artificial requests to the system. The rate and sizes ... -
Topics in stochastic simulation, with an application to seismic inversion
Hammer, Hugo Lewi (Doktoravhandlinger ved NTNU, 1503-8181; 2008:73, Doctoral thesis, 2008) -
Unraveling the Impact of Land Cover Changes on Climate Using Machine Learning and Explainable Artificial Intelligence
Kolevatova, Anastasiia; Riegler, Michael; Cherubini, Francesco; Hu, Xiangping; Hammer, Hugo Lewi (Peer reviewed; Journal article, 2021)A general issue in climate science is the handling of big data and running complex and computationally heavy simulations. In this paper, we explore the potential of using machine learning (ML) to spare computational time ...