47 citations · 67 across the 8 of their papers we have counts for
8 papers
The Sparse Tsetlin Machine: Sparse Representation with Active Literals
Sebastian Østby, Tobias M. Brambo, Sondre Glimsdal
This paper introduces the Sparse Tsetlin Machine (STM), a novel Tsetlin Machine (TM) that processes sparse data efficiently. Traditionally, the TM does not consider data characteri…
Green Tsetlin Redefining Efficiency in Tsetlin Machine Frameworks
Sondre Glimsdal, Sebastian Østby, Tobias M. Brambo +1
Green Tsetlin (GT) is a Tsetlin Machine (TM) framework developed to solve real-world problems using TMs. Several frameworks already exist that provide access to TM implementations.…
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…
Coalesced Multi-Output Tsetlin Machines with Clause Sharing
Sondre Glimsdal, Ole-Christoffer Granmo
Using finite-state machines to learn patterns, Tsetlin machines (TMs) have obtained competitive accuracy and learning speed across several benchmarks, with frugal memory- and energ…
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…
The Convolutional Tsetlin Machine
Ole-Christoffer Granmo, Sondre Glimsdal, Lei Jiao +3
Convolutional neural networks (CNNs) have obtained astounding successes for important pattern recognition tasks, but they suffer from high computational complexity and the lack of…
Thompson Sampling Guided Stochastic Searching on the Line for Deceptive Environments with Applications to Root-Finding Problems
Sondre Glimsdal, Ole-Christoffer Granmo
The multi-armed bandit problem forms the foundation for solving a wide range of on-line stochastic optimization problems through a simple, yet effective mechanism. One simply casts…
An Optimal Bayesian Network Based Solution Scheme for the Constrained Stochastic On-line Equi-Partitioning Problem
Sondre Glimsdal, Ole-Christoffer Granmo
A number of intriguing decision scenarios revolve around partitioning a collection of objects to optimize some application specific objective function. This problem is generally re…