98 citations · 339 across the 69 of their papers we have counts for
36 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
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…
On the Equivalence of the Weighted Tsetlin Machine and the Perceptron
Jivitesh Sharma, Ole-Christoffer Granmo, Lei Jiao
Tsetlin Machine (TM) has been gaining popularity as an inherently interpretable machine leaning method that is able to achieve promising performance with low computational complexi…
CostNet: An End-to-End Framework for Goal-Directed Reinforcement Learning
Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
Reinforcement Learning (RL) is a general framework concerned with an agent that seeks to maximize rewards in an environment. The learning typically happens through trial and error…
CaiRL: A High-Performance Reinforcement Learning Environment Toolkit
Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
This paper addresses the dire need for a platform that efficiently provides a framework for running reinforcement learning (RL) experiments. We propose the CaiRL Environment Toolki…
Interpretable Option Discovery using Deep Q-Learning and Variational Autoencoders
Per-Arne Andersen, Ole-Christoffer Granmo, Morten Goodwin
Deep Reinforcement Learning (RL) is unquestionably a robust framework to train autonomous agents in a wide variety of disciplines. However, traditional deep and shallow model-free…
Tsetlin Machine for Solving Contextual Bandit Problems
Raihan Seraj, Jivitesh Sharma, Ole-Christoffer Granmo
This paper introduces an interpretable contextual bandit algorithm using Tsetlin Machines, which solves complex pattern recognition tasks using propositional logic. The proposed ba…
On the Convergence of Tsetlin Machines for the AND and the OR Operators
Lei Jiao, Xuan Zhang, Ole-Christoffer Granmo
The Tsetlin Machine (TM) is a novel machine-learning algorithm based on propositional logic, which has obtained state-of-the-art performance on several pattern recognition problems…
Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin Machine
Jivitesh Sharma, Rohan Yadav, Ole-Christoffer Granmo +1
In this article, we introduce a novel variant of the Tsetlin machine (TM) that randomly drops clauses, the key learning elements of a TM. In effect, TM with drop clause ignores a r…