98 citations · 339 across the 69 of their papers we have counts for
12 papers · 1 filter
Efficient Data Fusion using the Tsetlin Machine
Rupsa Saha, Vladimir I. Zadorozhny, Ole-Christoffer Granmo
We propose a novel way of assessing and fusing noisy dynamic data using a Tsetlin Machine. Our approach consists in monitoring how explanations in form of logical clauses that a TM…
Harnessing Attention Mechanisms: Efficient Sequence Reduction using Attention-based Autoencoders
Daniel Biermann, Fabrizio Palumbo, Morten Goodwin +1
Many machine learning models use the manipulation of dimensions as a driving force to enable models to identify and learn important features in data. In the case of sequential data…
Contracting Tsetlin Machine with Absorbing Automata
Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao +4
In this paper, we introduce a sparse Tsetlin Machine (TM) with absorbing Tsetlin Automata (TA) states. In brief, the TA of each clause literal has both an absorbing Exclude- and an…
Generalized Convergence Analysis of Tsetlin Machines: A Probabilistic Approach to Concept Learning
Mohamed-Bachir Belaid, Jivitesh Sharma, Lei Jiao +3
Tsetlin Machines (TMs) have garnered increasing interest for their ability to learn concepts via propositional formulas and their proven efficiency across various application domai…
Learning Minimalistic Tsetlin Machine Clauses with Markov Boundary-Guided Pruning
Ole-Christoffer Granmo, Per-Arne Andersen, Lei Jiao +3
A set of variables is the Markov blanket of a random variable if it contains all the information needed for predicting the variable. If the blanket cannot be reduced without losing…
TMComposites: Plug-and-Play Collaboration Between Specialized Tsetlin Machines
Ole-Christoffer Granmo
Tsetlin Machines (TMs) provide a fundamental shift from arithmetic-based to logic-based machine learning. Supporting convolution, they deal successfully with image classification d…
An FPGA Architecture for Online Learning using the Tsetlin Machine
Samuel Prescott, Adrian Wheeldon, Rishad Shafik +3
There is a need for machine learning models to evolve in unsupervised circumstances. New classifications may be introduced, unexpected faults may occur, or the initial dataset may…
Energy-frugal and Interpretable AI Hardware Design using Learning Automata
Rishad Shafik, Tousif Rahman, Adrian Wheeldon +2
Energy efficiency is a crucial requirement for enabling powerful artificial intelligence applications at the microedge. Hardware acceleration with frugal architectural allocation i…
Verifying Properties of Tsetlin Machines
Emilia Przybysz, Bimal Bhattarai, Cosimo Persia +3
Tsetlin Machines (TsMs) are a promising and interpretable machine learning method which can be applied for various classification tasks. We present an exact encoding of TsMs into p…
Interpretable Tsetlin Machine-based Premature Ventricular Contraction Identification
Jinbao Zhang, Xuan Zhang, Lei Jiao +3
Neural network-based models have found wide use in automatic long-term electrocardiogram (ECG) analysis. However, such black box models are inadequate for analysing physiological s…
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…
Tsetlin Machine Embedding: Representing Words Using Logical Expressions
Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao +2
Embedding words in vector space is a fundamental first step in state-of-the-art natural language processing (NLP). Typical NLP solutions employ pre-defined vector representations t…