no code implementations • 19 Jan 2023 • K. Darshana Abeyrathna, Ahmed Abdulrahem Othman Abouzeid, Bimal Bhattarai, Charul Giri, Sondre Glimsdal, Ole-Christoffer Granmo, Lei Jiao, Rupsa Saha, Jivitesh Sharma, Svein Anders Tunheim, Xuan Zhang
This paper introduces a novel variant of TM learning - Clause Size Constrained TMs (CSC-TMs) - where one can set a soft constraint on the clause size.
6 code implementations • 7 Jan 2021 • Lei Jiao, Xuan Zhang, Ole-Christoffer Granmo, K. Darshana Abeyrathna
The Tsetlin Machine (TM) is a novel machine learning algorithm with several distinct properties, including transparent inference and learning using hardware-near building blocks.
2 code implementations • 10 Sep 2020 • K. Darshana Abeyrathna, Bimal Bhattarai, Morten Goodwin, Saeed Gorji, Ole-Christoffer Granmo, Lei Jiao, Rupsa Saha, Rohan K. Yadav
We evaluated the proposed parallelization across diverse learning tasks and it turns out that our decentralized TM learning algorithm copes well with working on outdated data, resulting in no significant loss in learning accuracy.
no code implementations • 4 Jul 2020 • K. Darshana Abeyrathna, Ole-Christoffer Granmo, Rishad Shafik, Alex Yakovlev, Adrian Wheeldon, Jie Lei, Morten Goodwin
However, TMs rely heavily on energy-costly random number generation to stochastically guide a team of Tsetlin Automata to a Nash Equilibrium of the TM game.
4 code implementations • 11 May 2020 • K. Darshana Abeyrathna, Ole-Christoffer Granmo, Morten Goodwin
Here, we address the accuracy-interpretability challenge in machine learning by equipping the TM clauses with integer weights.
4 code implementations • 4 Feb 2020 • K. Darshana Abeyrathna, Ole-Christoffer Granmo, Morten Goodwin
Although the RTM has solved non-linear regression problems with competitive accuracy, the resolution of the output is proportional to the number of clauses employed.
4 code implementations • 10 May 2019 • K. Darshana Abeyrathna, Ole-Christoffer Granmo, Xuan Zhang, Morten Goodwin
In this paper, we apply a new promising tool for pattern classification, namely, the Tsetlin Machine (TM), to the field of disease forecasting.
1 code implementation • 10 May 2019 • K. Darshana Abeyrathna, Ole-Christoffer Granmo, Lei Jiao, Morten Goodwin
We achieve this by: (1) using the conjunctive clauses of the TM to capture arbitrarily complex patterns; (2) mapping these patterns to a continuous output through a novel voting and normalization mechanism; and (3) employing a feedback scheme that updates the TM clauses to minimize the regression error.