
On the Convergence of Tsetlin Machines for the AND and the OR Operators
The Tsetlin Machine (TM) is a novel machinelearning algorithm based on ...
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Coalesced MultiOutput Tsetlin Machines with Clause Sharing
Using finitestate machines to learn patterns, Tsetlin machines (TMs) ha...
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Human Interpretable AI: Enhancing Tsetlin Machine Stochasticity with Drop Clause
In this article, we introduce a novel variant of the Tsetlin machine (TM...
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Explainable Tsetlin Machine framework for fake news detection with credibility score assessment
The proliferation of fake news, i.e., news intentionally spread for misi...
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Wordlevel Human Interpretable Scoring Mechanism for Novel Text Detection Using Tsetlin Machines
Recent research in novelty detection focuses mainly on documentlevel cl...
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Distributed Word Representation in Tsetlin Machine
Tsetlin Machine (TM) is an interpretable pattern recognition algorithm b...
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A Relational Tsetlin Machine with Applications to Natural Language Understanding
TMs are a pattern recognition approach that uses finite state machines f...
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LowPower Audio Keyword Spotting using Tsetlin Machines
The emergence of Artificial Intelligence (AI) driven Keyword Spotting (K...
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On the Convergence of Tsetlin Machines for the XOR Operator
The Tsetlin Machine (TM) is a novel machine learning algorithm with seve...
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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...
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Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost ConstantTime Scaling
Using logical clauses to represent patterns, Tsetlin machines (TMs) have...
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On the Convergence of Tsetlin Machines for the IDENTITY and NOT Operators
The Tsetlin Machine (TM) is a recent machine learning algorithm with sev...
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ClosedForm Expressions for Global and Local Interpretation of Tsetlin Machines with Applications to Explaining HighDimensional Data
Tsetlin Machines (TMs) capture patterns using conjunctive clauses in pro...
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A Novel MultiStep FiniteState Automaton for Arbitrarily Deterministic Tsetlin Machine Learning
Due to the high energy consumption and scalability challenges of deep le...
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Extending the Tsetlin Machine With IntegerWeighted Clauses for Increased Interpretability
Despite significant effort, building models that are both interpretable ...
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Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing
The Tsetlin Machine (TM) is a machine learning algorithm founded on the ...
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A Regression Tsetlin Machine with Integer Weighted Clauses for Compact Pattern Representation
The Regression Tsetlin Machine (RTM) addresses the lack of interpretabil...
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The Weighted Tsetlin Machine: Compressed Representations with Weighted Clauses
The Tsetlin Machine (TM) is an interpretable mechanism for pattern recog...
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A Tsetlin Machine with Multigranular Clauses
The recently introduced Tsetlin Machine (TM) has provided competitive pa...
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Environment Sound Classification using Multiple Feature Channels and Deep Convolutional Neural Networks
In this paper, we propose a model for the Environment Sound Classificati...
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Towards Modelbased Reinforcement Learning for Industrynear Environments
Deep reinforcement learning has over the past few years shown great pote...
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A Neural Turing Machine for Conditional Transition Graph Modeling
Graphs are an essential part of many machine learning problems such as a...
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The Convolutional Tsetlin Machine
Deep neural networks have obtained astounding successes for important pa...
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The Regression Tsetlin Machine: A Tsetlin Machine for Continuous Output Problems
The recently introduced Tsetlin Machine (TM) has provided competitive pa...
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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,...
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The Dreaming Variational Autoencoder for Reinforcement Learning Environments
Reinforcement learning has shown great potential in generalizing over ra...
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Using the Tsetlin Machine to Learn HumanInterpretable Rules for HighAccuracy Text Categorization with Medical Applications
Medical applications challenge today's text categorization techniques by...
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Deep RTS: A Game Environment for Deep Reinforcement Learning in RealTime Strategy Games
Reinforcement learning (RL) is an area of research that has blossomed tr...
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The Tsetlin Machine  A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic
Although simple individually, artificial neurons provide stateofthear...
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FlashRL: A Reinforcement Learning Platform for Flash Games
Reinforcement Learning (RL) is a research area that has blossomed tremen...
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Towards a Deep Reinforcement Learning Approach for Tower Line Wars
There have been numerous breakthroughs with reinforcement learning in th...
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Thompson Sampling Guided Stochastic Searching on the Line for Deceptive Environments with Applications to RootFinding Problems
The multiarmed bandit problem forms the foundation for solving a wide r...
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An Optimal Bayesian Network Based Solution Scheme for the Constrained Stochastic Online EquiPartitioning Problem
A number of intriguing decision scenarios revolve around partitioning a ...
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Bayesian Unification of Gradient and Banditbased Learning for Accelerated Global Optimisation
Bandit based optimisation has a remarkable advantage over gradient based...
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OleChristoffer Granmo
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