5 citations · 9 across the 5 of their papers we have counts for
5 papers
A Novel Multi-Step Finite-State Automaton for Arbitrarily Deterministic Tsetlin Machine Learning
K. Darshana Abeyrathna, Ole-Christoffer Granmo, Rishad Shafik +4
Due to the high energy consumption and scalability challenges of deep learning, there is a critical need to shift research focus towards dealing with energy consumption constraints…
Extending the Tsetlin Machine With Integer-Weighted Clauses for Increased Interpretability
K. Darshana Abeyrathna, Ole-Christoffer Granmo, Morten Goodwin
Despite significant effort, building models that are both interpretable and accurate is an unresolved challenge for many pattern recognition problems. In general, rule-based and li…
Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing
Saeed Rahimi Gorji, Ole-Christoffer Granmo, Sondre Glimsdal +2
The Tsetlin Machine (TM) is a machine learning algorithm founded on the classical Tsetlin Automaton (TA) and game theory. It further leverages frequent pattern mining and resource…
A Regression Tsetlin Machine with Integer Weighted Clauses for Compact Pattern Representation
K. Darshana Abeyrathna, Ole-Christoffer Granmo, Morten Goodwin
The Regression Tsetlin Machine (RTM) addresses the lack of interpretability impeding state-of-the-art nonlinear regression models. It does this by using conjunctive clauses in prop…
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation
Erlend Olsvik, Christian M. D. Trinh, Kristian Muri Knausgård +5
Our understanding and ability to effectively monitor and manage coastal ecosystems are severely limited by observation methods. Automatic recognition of species in natural environm…