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Found 8 papers, 4 papers with code
Green Tsetlin Redefining Efficiency in Tsetlin Machine Frameworks
GT is an easy-to-use framework that aims to lower the complexity and provide a production-ready TM implementation that is great for experienced practitioners and beginners.
The Sparse Tsetlin Machine: Sparse Representation with Active Literals
This paper introduces the Sparse Tsetlin Machine (STM), a novel Tsetlin Machine (TM) that processes sparse data efficiently.
Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Size
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.
Coalesced Multi-Output Tsetlin Machines with Clause Sharing
While TM and CoTM accuracy is similar when using more than $1$K clauses per class, CoTM reaches peak accuracy $3\times$ faster on MNIST with $8$K clauses.
Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing
Instead we use a simple look-up table that indexes the clauses on the features that falsify them.
The Convolutional Tsetlin Machine
Whereas the TM categorizes an image by employing each clause once to the whole image, the CTM uses each clause as a convolution filter.
Ranked #14 on
Image Classification
on Fashion-MNIST
Thompson Sampling Guided Stochastic Searching on the Line for Deceptive Environments with Applications to Root-Finding Problems
In this paper, we address a particularly intriguing variant of the multi-armed bandit problem, referred to as the {\it Stochastic Point Location (SPL) Problem}.
An Optimal Bayesian Network Based Solution Scheme for the Constrained Stochastic On-line Equi-Partitioning Problem
This problem is generally referred to as the Object Partitioning Problem (OPP) and is known to be NP-hard.
