research
∙
09/12/2023
Learning Minimalistic Tsetlin Machine Clauses with Markov Boundary-Guided Pruning
A set of variables is the Markov blanket of a random variable if it cont...
share
research
∙
09/09/2023
TMComposites: Plug-and-Play Collaboration Between Specialized Tsetlin Machines
Tsetlin Machines (TMs) provide a fundamental shift from arithmetic-based...
research
∙
06/01/2023
An FPGA Architecture for Online Learning using the Tsetlin Machine
There is a need for machine learning models to evolve in unsupervised ci...
research
∙
05/19/2023
Energy-frugal and Interpretable AI Hardware Design using Learning Automata
Energy efficiency is a crucial requirement for enabling powerful artific...
research
∙
03/25/2023
Verifying Properties of Tsetlin Machines
Tsetlin Machines (TsMs) are a promising and interpretable machine learni...
research
∙
01/20/2023
Interpretable Tsetlin Machine-based Premature Ventricular Contraction Identification
Neural network-based models have found wide use in automatic long-term e...
research
∙
01/19/2023
Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Size
Tsetlin machine (TM) is a logic-based machine learning approach with the...
research
∙
01/02/2023
Tsetlin Machine Embedding: Representing Words Using Logical Expressions
Embedding words in vector space is a fundamental first step in state-of-...
research
∙
12/27/2022
On the Equivalence of the Weighted Tsetlin Machine and the Perceptron
Tsetlin Machine (TM) has been gaining popularity as an inherently interp...
research
∙
10/03/2022
CaiRL: A High-Performance Reinforcement Learning Environment Toolkit
This paper addresses the dire need for a platform that efficiently provi...
research
∙
10/03/2022
CostNet: An End-to-End Framework for Goal-Directed Reinforcement Learning
Reinforcement Learning (RL) is a general framework concerned with an age...
research
∙
10/03/2022
Interpretable Option Discovery using Deep Q-Learning and Variational Autoencoders
Deep Reinforcement Learning (RL) is unquestionably a robust framework to...
research
∙
08/30/2022
Towards Artificial Virtuous Agents: Games, Dilemmas and Machine Learning
Machine ethics has received increasing attention over the past few years...
research
∙
03/23/2022
Socially Fair Mitigation of Misinformation on Social Networks via Constraint Stochastic Optimization
Recent social networks' misinformation mitigation approaches tend to inv...
research
∙
03/08/2022
Logic-based AI for Interpretable Board Game Winner Prediction with Tsetlin Machine
Hex is a turn-based two-player connection game with a high branching fac...
research
∙
02/04/2022
Tsetlin Machine for Solving Contextual Bandit Problems
This paper introduces an interpretable contextual bandit algorithm using...
research
∙
09/17/2021
On the Convergence of Tsetlin Machines for the AND and the OR Operators
The Tsetlin Machine (TM) is a novel machine-learning algorithm based on ...
research
∙
08/17/2021
Coalesced Multi-Output Tsetlin Machines with Clause Sharing
Using finite-state machines to learn patterns, Tsetlin machines (TMs) ha...
research
∙
05/30/2021
Human Interpretable AI: Enhancing Tsetlin Machine Stochasticity with Drop Clause
In this article, we introduce a novel variant of the Tsetlin machine (TM...
research
∙
05/19/2021
Explainable Tsetlin Machine framework for fake news detection with credibility score assessment
The proliferation of fake news, i.e., news intentionally spread for misi...
research
∙
05/10/2021
Word-level Human Interpretable Scoring Mechanism for Novel Text Detection Using Tsetlin Machines
Recent research in novelty detection focuses mainly on document-level cl...
research
∙
04/14/2021
Distributed Word Representation in Tsetlin Machine
Tsetlin Machine (TM) is an interpretable pattern recognition algorithm b...
research
∙
02/22/2021
A Relational Tsetlin Machine with Applications to Natural Language Understanding
TMs are a pattern recognition approach that uses finite state machines f...
research
∙
01/27/2021
Low-Power Audio Keyword Spotting using Tsetlin Machines
The emergence of Artificial Intelligence (AI) driven Keyword Spotting (K...
research
∙
01/07/2021
On the Convergence of Tsetlin Machines for the XOR Operator
The Tsetlin Machine (TM) is a novel machine learning algorithm with seve...
research
∙
11/17/2020
Measuring the Novelty of Natural Language Text Using the Conjunctive Clauses of a Tsetlin Machine Text Classifier
Most supervised text classification approaches assume a closed world, co...
research
∙
09/10/2020
Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling
Using logical clauses to represent patterns, Tsetlin machines (TMs) have...
research
∙
07/28/2020
On the Convergence of Tsetlin Machines for the IDENTITY- and NOT Operators
The Tsetlin Machine (TM) is a recent machine learning algorithm with sev...
research
∙
07/27/2020
Closed-Form Expressions for Global and Local Interpretation of Tsetlin Machines with Applications to Explaining High-Dimensional Data
Tsetlin Machines (TMs) capture patterns using conjunctive clauses in pro...
research
∙
07/04/2020
A Novel Multi-Step Finite-State Automaton for Arbitrarily Deterministic Tsetlin Machine Learning
Due to the high energy consumption and scalability challenges of deep le...
research
∙
05/11/2020
Extending the Tsetlin Machine With Integer-Weighted Clauses for Increased Interpretability
Despite significant effort, building models that are both interpretable ...
research
∙
04/07/2020
Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing
The Tsetlin Machine (TM) is a machine learning algorithm founded on the ...
research
∙
02/04/2020
A Regression Tsetlin Machine with Integer Weighted Clauses for Compact Pattern Representation
The Regression Tsetlin Machine (RTM) addresses the lack of interpretabil...
research
∙
11/28/2019
The Weighted Tsetlin Machine: Compressed Representations with Weighted Clauses
The Tsetlin Machine (TM) is an interpretable mechanism for pattern recog...
research
∙
09/16/2019
A Tsetlin Machine with Multigranular Clauses
The recently introduced Tsetlin Machine (TM) has provided competitive pa...
research
∙
08/28/2019
Environment Sound Classification using Multiple Feature Channels and Deep Convolutional Neural Networks
In this paper, we propose a model for the Environment Sound Classificati...
research
∙
07/27/2019
Towards Model-based Reinforcement Learning for Industry-near Environments
Deep reinforcement learning has over the past few years shown great pote...
research
∙
07/15/2019
A Neural Turing Machine for Conditional Transition Graph Modeling
Graphs are an essential part of many machine learning problems such as a...
research
∙
05/23/2019
The Convolutional Tsetlin Machine
Deep neural networks have obtained astounding successes for important pa...
research
∙
05/10/2019
The Regression Tsetlin Machine: A Tsetlin Machine for Continuous Output Problems
The recently introduced Tsetlin Machine (TM) has provided competitive pa...
research
∙
05/10/2019
A Scheme for Continuous Input to the Tsetlin Machine with Applications to Forecasting Disease Outbreaks
In this paper, we apply a new promising tool for pattern classification,...
research
∙
10/02/2018
The Dreaming Variational Autoencoder for Reinforcement Learning Environments
Reinforcement learning has shown great potential in generalizing over ra...
research
∙
09/12/2018
Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications
Medical applications challenge today's text categorization techniques by...
research
∙
08/15/2018
Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games
Reinforcement learning (RL) is an area of research that has blossomed tr...
research
∙
04/04/2018
The Tsetlin Machine - A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic
Although simple individually, artificial neurons provide state-of-the-ar...
research
∙
01/26/2018
FlashRL: A Reinforcement Learning Platform for Flash Games
Reinforcement Learning (RL) is a research area that has blossomed tremen...
research
∙
12/17/2017
Towards a Deep Reinforcement Learning Approach for Tower Line Wars
There have been numerous breakthroughs with reinforcement learning in th...
research
∙
08/05/2017
Thompson Sampling Guided Stochastic Searching on the Line for Deceptive Environments with Applications to Root-Finding Problems
The multi-armed bandit problem forms the foundation for solving a wide r...
research
∙
07/11/2017
An Optimal Bayesian Network Based Solution Scheme for the Constrained Stochastic On-line Equi-Partitioning Problem
A number of intriguing decision scenarios revolve around partitioning a ...
research
∙
05/28/2017
