1 citations · 2 across the 6 of their papers we have counts for
9 papers
CaiRL: A High-Performance Reinforcement Learning Environment Toolkit
Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
This paper addresses the dire need for a platform that efficiently provides a framework for running reinforcement learning (RL) experiments. We propose the CaiRL Environment Toolki…
Interpretable Option Discovery using Deep Q-Learning and Variational Autoencoders
Per-Arne Andersen, Ole-Christoffer Granmo, Morten Goodwin
Deep Reinforcement Learning (RL) is unquestionably a robust framework to train autonomous agents in a wide variety of disciplines. However, traditional deep and shallow model-free…
Socially Fair Mitigation of Misinformation on Social Networks via Constraint Stochastic Optimization
Ahmed Abouzeid, Ole-Christoffer Granmo, Christian Webersik +1
Recent social networks' misinformation mitigation approaches tend to investigate how to reduce misinformation by considering a whole-network statistical scale. However, unbalanced…
Enhancing Interpretable Clauses Semantically using Pretrained Word Representation
Rohan Kumar Yadav, Lei Jiao, Ole-Christoffer Granmo +1
Tsetlin Machine (TM) is an interpretable pattern recognition algorithm based on propositional logic, which has demonstrated competitive performance in many Natural Language Process…
Improving prostate whole gland segmentation in t2-weighted MRI with synthetically generated data
Alvaro Fernandez-Quilez, Steinar Valle Larsen, Morten Goodwin +3
Whole gland (WG) segmentation of the prostate plays a crucial role in detection, staging and treatment planning of prostate cancer (PCa). Despite promise shown by deep learning (DL…
A Relational Tsetlin Machine with Applications to Natural Language Understanding
Rupsa Saha, Ole-Christoffer Granmo, Vladimir I. Zadorozhny +1
TMs are a pattern recognition approach that uses finite state machines for learning and propositional logic to represent patterns. In addition to being natively interpretable, they…