26 citations · 43 across the 5 of their papers we have counts for
6 papers
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 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…
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…
cleanTS: Automated (AutoML) Tool to Clean Univariate Time Series at Microscales
Mayur Kishor Shende, Andres E. Feijoo-Lorenzo, Neeraj Dhanraj Bokde
Data cleaning is one of the most important tasks in data analysis processes. One of the perennial challenges in data analytics is the detection and handling of non-valid data. Fail…
An empirical estimation for time and memory algorithm complexities: newly developed R package
Marc Agenis-Nevers, Neeraj Dhanraj Bokde, Zaher Mundher Yaseen +1
This article introduces GuessCompx which is an R package that performs an empirical estimation on the time and memory complexities of an algorithm or a function. It tests multiple…