19 citations · 41 across the 18 of their papers we have counts for
18 papers
MapAI: Precision in Building Segmentation
Sander Riisøen Jyhne, Morten Goodwin, Per Arne Andersen +5
MapAI: Precision in Building Segmentation is a competition arranged with the Norwegian Artificial Intelligence Research Consortium (NORA) in collaboration with Centre for Artificia…
Contracting Tsetlin Machine with Absorbing Automata
Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao +4
In this paper, we introduce a sparse Tsetlin Machine (TM) with absorbing Tsetlin Automata (TA) states. In brief, the TA of each clause literal has both an absorbing Exclude- and an…
Generalized Convergence Analysis of Tsetlin Machines: A Probabilistic Approach to Concept Learning
Mohamed-Bachir Belaid, Jivitesh Sharma, Lei Jiao +3
Tsetlin Machines (TMs) have garnered increasing interest for their ability to learn concepts via propositional formulas and their proven efficiency across various application domai…
Learning Minimalistic Tsetlin Machine Clauses with Markov Boundary-Guided Pruning
Ole-Christoffer Granmo, Per-Arne Andersen, Lei Jiao +3
A set of variables is the Markov blanket of a random variable if it contains all the information needed for predicting the variable. If the blanket cannot be reduced without losing…
DeNISE: Deep Networks for Improved Segmentation Edges
Sander Riisøen Jyhne, Per-Arne Andersen, Morten Goodwin
This paper presents Deep Networks for Improved Segmentation Edges (DeNISE), a novel data enhancement technique using edge detection and segmentation models to improve the boundary…
Loss- and Reward-Weighting for Efficient Distributed Reinforcement Learning
Martin Holen, Per-Arne Andersen, Kristian Muri Knausgård +1
This paper introduces two learning schemes for distributed agents in Reinforcement Learning (RL) environments, namely Reward-Weighted (R-Weighted) and Loss-Weighted (L-Weighted) gr…