21 citations · 58 across the 11 of their papers we have counts for
11 papers
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…
Pruning Literals for Highly Efficient Explainability at Word Level
Rohan Kumar Yadav, Bimal Bhattarai, Abhik Jana +2
Designing an explainable model becomes crucial now for Natural Language Processing(NLP) since most of the state-of-the-art machine learning models provide a limited explanation for…
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…
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…
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…
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…
Explainable Tsetlin Machine framework for fake news detection with credibility score assessment
Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao
The proliferation of fake news, i.e., news intentionally spread for misinformation, poses a threat to individuals and society. Despite various fact-checking websites such as Politi…
Word-level Human Interpretable Scoring Mechanism for Novel Text Detection Using Tsetlin Machines
Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao
Recent research in novelty detection focuses mainly on document-level classification, employing deep neural networks (DNN). However, the black-box nature of DNNs makes it difficult…
Measuring the Novelty of Natural Language Text Using the Conjunctive Clauses of a Tsetlin Machine Text Classifier
Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao
Most supervised text classification approaches assume a closed world, counting on all classes being present in the data at training time. This assumption can lead to unpredictable…
Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling
K. Darshana Abeyrathna, Bimal Bhattarai, Morten Goodwin +5
Using logical clauses to represent patterns, Tsetlin Machines (TMs) have recently obtained competitive performance in terms of accuracy, memory footprint, energy, and learning spee…