172 citations
- University of BergenNO8 papers
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- University of MinnesotaUS4 papers
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9 papers · 1 filter
A Comparison Between Tsetlin Machines and Deep Neural Networks in the Context of Recommendation Systems
Karl Audun Borgersen, Morten Goodwin, Jivitesh Sharma
Recommendation Systems (RSs) are ubiquitous in modern society and are one of the largest points of interaction between humans and AI. Modern RSs are often implemented using deep le…
Towards Artificial Virtuous Agents: Games, Dilemmas and Machine Learning
Ajay Vishwanath, Einar Duenger Bøhn, Ole-Christoffer Granmo +2
Machine ethics has received increasing attention over the past few years because of the need to ensure safe and reliable artificial intelligence (AI). The two dominantly used theor…
Clustering in Recurrent Neural Networks for Micro-Segmentation using Spending Personality
Charl Maree, Christian W. Omlin
Customer segmentation has long been a productive field in banking. However, with new approaches to traditional problems come new opportunities. Fine-grained customer segments are n…
Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling
K. Darshana Abeyrathna, Bimal Bhattarai, Morten Goodwin +5
Using logical clauses to represent patterns, Tsetlin Machines (TMs) have recently obtained competitive performance in terms of accuracy, memory footprint, energy, and learning spee…
On the Convergence of Tsetlin Machines for the IDENTITY- and NOT Operators
Xuan Zhang, Lei Jiao, Ole-Christoffer Granmo +1
The Tsetlin Machine (TM) is a recent machine learning algorithm with several distinct properties, such as interpretability, simplicity, and hardware-friendliness. Although numerous…
Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games
Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
Reinforcement learning (RL) is an area of research that has blossomed tremendously in recent years and has shown remarkable potential for artificial intelligence based opponents in…