98 citations · 339 across the 69 of their papers we have counts for
74 papers
CRISP: A Framework for Clause-Reconstructed Interpretable NeuroSymbolic Propositions
Alex Chan, Shafi Muhtasim Chowdhury, Ekin Can Erkuş +3
Deep neural networks achieve high accuracy through layered numerical transformations, yet their decisions remain difficult to audit because decision evidence is encoded in hidden a…
Leveraging Interpretable Tsetlin Machine for PDF Malware Detection
Rahul Jaiswal, Ole-Christoffer Granmo
In the digital era, Portable Document Format (PDF) is one of the most widely used file formats for storing and exchanging digital documents due to its platform independence and ric…
On-Device Interpretable Tsetlin Machine-Based Intrusion Detection for Secure IoMT
Rahul Jaiswal, Per-Arne Andersen, Linga Reddy Cenkeramaddi +2
The rapid evolution of digital health technologies is redefining healthcare services worldwide. The integration of wireless communication and Internet-enabled medical devices withi…
FastOmniTMAE: Parallel Clause Learning for Scalable and Hardware-Efficient Tsetlin Embeddings
Ahmed K. Kadhim, Lei Jiao, Rishad Shafik +2
Embedding models in natural language processing (NLP) increasingly rely on deep architectures such as BERT, while simpler models such as Word2Vec provide efficient representations…
A Tsetlin Machine-driven Intrusion Detection System for Next-Generation IoMT Security
Rahul Jaiswal, Per-Arne Andersen, Linga Reddy Cenkeramaddi +2
The rapid adoption of the Internet of Medical Things (IoMT) is transforming healthcare by enabling seamless connectivity among medical devices, systems, and services. However, it a…
Explainable and Hardware-Efficient Jamming Detection for 5G Networks Using the Convolutional Tsetlin Machine
Vojtech Halenka, Mohammadreza Amini, Per-Arne Andersen +2
All applications in fifth-generation (5G) networks rely on stable radio-frequency (RF) environments to support mission-critical services in mobility, automation, and connected inte…
Scalable Bayesian Network Structure Learning Using Tsetlin Machine to Constrain the Search Space
Kunal Dumbre, Lei Jiao, Ole-Christoffer Granmo
The PC algorithm is a widely used method in causal inference for learning the structure of Bayesian networks. Despite its popularity, the PC algorithm suffers from significant time…
A Methodology for Transparent Logic-Based Classification Using a Multi-Task Convolutional Tsetlin Machine
Mayur Kishor Shende, Ole-Christoffer Granmo, Runar Helin +2
The Tsetlin Machine (TM) is a novel machine learning paradigm that employs finite-state automata for learning and utilizes propositional logic to represent patterns. Due to its sim…
A Comparative Study of Feature Selection in Tsetlin Machines
Vojtech Halenka, Ole-Christoffer Granmo, Lei Jiao +1
Feature Selection (FS) is crucial for improving model interpretability, reducing complexity, and sometimes for enhancing accuracy. The recently introduced Tsetlin machine (TM) offe…
Uncertainty Quantification in the Tsetlin Machine
Runar Helin, Ole-Christoffer Granmo, Mayur Kishor Shende +5
Data modeling using Tsetlin machines (TMs) is all about building logical rules from the data features. The decisions of the model are based on a combination of these logical rules.…
The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs
Ole-Christoffer Granmo, Youmna Abdelwahab, Per-Arne Andersen +12
Pattern recognition with concise and flat AND-rules makes the Tsetlin Machine (TM) both interpretable and efficient, while the power of Tsetlin automata enables accuracy comparable…
Omni TM-AE: A Scalable and Interpretable Embedding Model Using the Full Tsetlin Machine State Space
Ahmed K. Kadhim, Lei Jiao, Rishad Shafik +1
The increasing complexity of large-scale language models has amplified concerns regarding their interpretability and reusability. While traditional embedding models like Word2Vec a…
An All-digital 8.6-nJ/Frame 65-nm Tsetlin Machine Image Classification Accelerator
Svein Anders Tunheim, Yujin Zheng, Lei Jiao +3
We present an all-digital programmable machine learning accelerator chip for image classification, underpinning on the Tsetlin machine (TM) principles. The TM is an emerging machin…
Scalable Multi-phase Word Embedding Using Conjunctive Propositional Clauses
Ahmed K. Kadhim, Lei Jiao, Rishad Shafik +2
The Tsetlin Machine (TM) architecture has recently demonstrated effectiveness in Machine Learning (ML), particularly within Natural Language Processing (NLP). It has been utilized…
Adversarial Attacks on AI-Generated Text Detection Models: A Token Probability-Based Approach Using Embeddings
Ahmed K. Kadhim, Lei Jiao, Rishad Shafik +1
In recent years, text generation tools utilizing Artificial Intelligence (AI) have occasionally been misused across various domains, such as generating student reports or creative…
Exploring State Space and Reasoning by Elimination in Tsetlin Machines
Ahmed K. Kadhim, Ole-Christoffer Granmo, Lei Jiao +1
The Tsetlin Machine (TM) has gained significant attention in Machine Learning (ML). By employing logical fundamentals, it facilitates pattern learning and representation, offering…
Exploring Effects of Hyperdimensional Vectors for Tsetlin Machines
Vojtech Halenka, Ahmed K. Kadhim, Paul F. A. Clarke +5
Tsetlin machines (TMs) have been successful in several application domains, operating with high efficiency on Boolean representations of the input data. However, Booleanizing compl…
An Optimized Toolbox for Advanced Image Processing with Tsetlin Machine Composites
Ylva Grønningsæter, Halvor S. Smørvik, Ole-Christoffer Granmo
The Tsetlin Machine (TM) has achieved competitive results on several image classification benchmarks, including MNIST, K-MNIST, F-MNIST, and CIFAR-2. However, color image classific…
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…