5 citations · 7 across the 7 of their papers we have counts for
7 papers
Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Size
K. Darshana Abeyrathna, Ahmed Abdulrahem Othman Abouzeid, Bimal Bhattarai +8
Tsetlin machine (TM) is a logic-based machine learning approach with the crucial advantages of being transparent and hardware-friendly. While TMs match or surpass deep learning acc…
On the Convergence of Tsetlin Machines for the XOR Operator
Lei Jiao, Xuan Zhang, Ole-Christoffer Granmo +1
The Tsetlin Machine (TM) is a novel machine learning algorithm with several distinct properties, including transparent inference and learning using hardware-near building blocks. A…
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…
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…
The Regression Tsetlin Machine: A Tsetlin Machine for Continuous Output Problems
K. Darshana Abeyrathna, Ole-Christoffer Granmo, Lei Jiao +1
The recently introduced Tsetlin Machine (TM) has provided competitive pattern classification accuracy in several benchmarks, composing patterns with easy-to-interpret conjunctive c…