no code implementations • 3 Oct 2023 • Mohamed-Bachir Belaid, Jivitesh Sharma, Lei Jiao, Ole-Christoffer Granmo, Per-Arne Andersen, Anis Yazidi
Tsetlin Machines (TMs) have garnered increasing interest for their ability to learn concepts via propositional formulas and their proven efficiency across various application domains.
1 code implementation • 30 Aug 2023 • Karl Audun Kagnes Borgersen, Morten Goodwin, Jivitesh Sharma, Tobias Aasmoe, Mari Leonhardsen, Gro Herredsvela Rørvik
In this paper, we evaluate the viability of the image embeddings from numerous pre-trained computer vision models using a novel approach named CorrEmbed.
1 code implementation • 25 Mar 2023 • Emilia Przybysz, Bimal Bhattarai, Cosimo Persia, Ana Ozaki, Ole-Christoffer Granmo, Jivitesh Sharma
Then, we show the correctness of our encoding and provide results for the properties: adversarial robustness, equivalence, and similarity of TsMs.
no code implementations • 19 Jan 2023 • K. Darshana Abeyrathna, Ahmed Abdulrahem Othman Abouzeid, Bimal Bhattarai, Charul Giri, Sondre Glimsdal, Ole-Christoffer Granmo, Lei Jiao, Rupsa Saha, Jivitesh Sharma, Svein Anders Tunheim, Xuan Zhang
This paper introduces a novel variant of TM learning - Clause Size Constrained TMs (CSC-TMs) - where one can set a soft constraint on the clause size.
1 code implementation • 2 Jan 2023 • Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao, Rohan Yadav, Jivitesh Sharma
We also visualize word clusters in vector space, demonstrating how our logical embedding co-locate similar words.
no code implementations • 27 Dec 2022 • 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 complexity on a variety of applications.
1 code implementation • 20 Dec 2022 • Karl Audun Borgersen, Morten Goodwin, Jivitesh Sharma
These comparisons are based on model performance, interpretability/explainability, and scalability.
1 code implementation • 4 Feb 2022 • 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.
6 code implementations • 30 May 2021 • Jivitesh Sharma, Rohan Yadav, Ole-Christoffer Granmo, Lei Jiao
In this article, we introduce a novel variant of the Tsetlin machine (TM) that randomly drops clauses, the key learning elements of a TM.
no code implementations • 28 Aug 2019 • 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 Network (CNN) with Attention mechanism.
no code implementations • 23 May 2019 • Jivitesh Sharma, Per-Arne Andersen, Ole-Chrisoffer Granmo, Morten Goodwin
We also propose a new reinforcement learning approach that entails pretraining the network weights of a DQN based agents to incorporate information on the shortest path to the exit.