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Found 27 papers, 14 papers with code
ConvTextTM: An Explainable Convolutional Tsetlin Machine Framework for Text Classification
Recent advancements in natural language processing (NLP) have reshaped the industry, with powerful language models such as GPT-3 achieving superhuman performance on various tasks.
Ranked #1 on
Fake News Detection
on PolitiFact
Contracting Tsetlin Machine with Absorbing Automata
In brief, the TA of each clause literal has both an absorbing Exclude- and an absorbing Include state, making the learning scheme absorbing instead of ergodic.
Generalized Convergence Analysis of Tsetlin Machines: A Probabilistic Approach to Concept Learning
Tsetlin Machines (TMs) have garnered increasing interest for their ability to learn concepts via propositional formulas and their proven efficiency across various application domains.
Learning Minimalistic Tsetlin Machine Clauses with Markov Boundary-Guided Pruning
A set of variables is the Markov blanket of a random variable if it contains all the information needed for predicting the variable.
When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Framework
In this paper, we advocate CPN-FedSL, a novel and flexible Federated Split Learning (FedSL) framework over Computing Power Network (CPN).
Interpretable Tsetlin Machine-based Premature Ventricular Contraction Identification
In this study, we develop a Tsetlin machine (TM) based architecture for premature ventricular contraction (PVC) identification by analysing long-term ECG signals.
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.
Tsetlin Machine Embedding: Representing Words Using Logical Expressions
We also visualize word clusters in vector space, demonstrating how our logical embedding co-locate similar words.
On the Equivalence of the Weighted Tsetlin Machine and the Perceptron
Tsetlin Machine (TM) has been gaining popularity as an inherently interpretable machine leaning method that is able to achieve promising performance with low computational complexity on a variety of applications.
Deep Crowd Anomaly Detection: State-of-the-Art, Challenges, and Future Research Directions
Crowd anomaly detection is one of the most popular topics in computer vision in the context of smart cities.
Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook
We provide insight into popular deep learning approaches for ecological data analysis in plain language, focusing on the techniques of supervised learning with deep neural networks, and illustrate challenges and opportunities through established and emerging applications of deep learning to marine ecology.
On the Convergence of Tsetlin Machines for the AND and the OR Operators
The analyses on AND and OR operators, together with the previously analysed 1-bit and XOR operations, complete the convergence analyses on basic operators in Boolean algebra.
Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin Machine
In this article, we introduce a novel variant of the Tsetlin machine (TM) that randomly drops clauses, the key learning elements of a TM.
Explainable Tsetlin Machine framework for fake news detection with credibility score assessment
The proliferation of fake news, i. e., news intentionally spread for misinformation, poses a threat to individuals and society.
Word-level Human Interpretable Scoring Mechanism for Novel Text Detection Using Tsetlin Machines
Our approach encodes a description of the novel documents using the linguistic patterns captured by TM clauses.
Enhancing Interpretable Clauses Semantically using Pretrained Word Representation
The approach significantly enhances the performance and interpretability of TM.
Ranked #8 on
Text Classification
on R52
Arena-Rosnav: Towards Deployment of Deep-Reinforcement-Learning-Based Obstacle Avoidance into Conventional Autonomous Navigation Systems
Recently, mobile robots have become important tools in various industries, especially in logistics.
On the Convergence of Tsetlin Machines for the XOR Operator
The Tsetlin Machine (TM) is a novel machine learning algorithm with several distinct properties, including transparent inference and learning using hardware-near building blocks.
Measuring the Novelty of Natural Language Text Using the Conjunctive Clauses of a Tsetlin Machine Text Classifier
The mechanism uses the conjunctive clauses of the TM to measure to what degree a text matches the classes covered by the training data.
Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling
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.
On the Convergence of Tsetlin Machines for the IDENTITY- and NOT Operators
The analysis of the convergence of the two basic operators lays the foundation for analyzing other logical operators.
Temperate Fish Detection and Classification: a Deep Learning based Approach
In this paper, we propose a two-step deep learning approach for the detection and classification of temperate fishes without pre-filtering.
Interpretability in Word Sense Disambiguation using Tsetlin Machine
Word Sense Disambiguation (WSD) is a longstanding unresolved task in Natural Language Processing.
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
The Regression Tsetlin Machine: A Tsetlin Machine for Continuous Output Problems
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.
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation
The second step is to train the classifier based on a new data set consisting of species that we are interested in for classification, named as post-training.
Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications
The Tsetlin Machine either performs on par with or outperforms all of the evaluated methods on both the 20 Newsgroups and IMDb datasets, as well as on a non-public clinical dataset.
