8 citations · 25 across the 11 of their papers we have counts for
11 papers
Generalized Convergence Analysis of Tsetlin Machines: A Probabilistic Approach to Concept Learning
Mohamed-Bachir Belaid, Jivitesh Sharma, Lei Jiao +3
Tsetlin Machines (TMs) have garnered increasing interest for their ability to learn concepts via propositional formulas and their proven efficiency across various application domai…
CorrEmbed: Evaluating Pre-trained Model Image Similarity Efficacy with a Novel Metric
Karl Audun Kagnes Borgersen, Morten Goodwin, Jivitesh Sharma +3
Detecting visually similar images is a particularly useful attribute to look to when calculating product recommendations. Embedding similarity, which utilizes pre-trained computer…
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…
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…
A Comparison Between Tsetlin Machines and Deep Neural Networks in the Context of Recommendation Systems
Karl Audun Borgersen, Morten Goodwin, Jivitesh Sharma
Recommendation Systems (RSs) are ubiquitous in modern society and are one of the largest points of interaction between humans and AI. Modern RSs are often implemented using deep le…
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…
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…
Environment Sound Classification using Multiple Feature Channels and Attention based Deep Convolutional Neural Network
Jivitesh Sharma, Ole-Christoffer Granmo, Morten Goodwin
In this paper, we propose a model for the Environment Sound Classification Task (ESC) that consists of multiple feature channels given as input to a Deep Convolutional Neural Netwo…
Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment
Jivitesh Sharma, Per-Arne Andersen, Ole-Chrisoffer Granmo +1
We focus on the important problem of emergency evacuation, which clearly could benefit from reinforcement learning that has been largely unaddressed. Emergency evacuation is a comp…