Search Results for author: Jivitesh Sharma

Found 11 papers, 6 papers with code

Generalized Convergence Analysis of Tsetlin Machines: A Probabilistic Approach to Concept Learning

no code implementations3 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.

Interpretable Machine Learning

CorrEmbed: Evaluating Pre-trained Model Image Similarity Efficacy with a Novel Metric

1 code implementation30 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.

Attribute Image Similarity Search +1

Verifying Properties of Tsetlin Machines

1 code implementation25 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.

Adversarial Robustness Interpretable Machine Learning +2

On the Equivalence of the Weighted Tsetlin Machine and the Perceptron

no code implementations27 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.

Tsetlin Machine for Solving Contextual Bandit Problems

1 code implementation4 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.

Thompson Sampling

Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin Machine

6 code implementations30 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.

Image Classification Interpretable Machine Learning

Environment Sound Classification using Multiple Feature Channels and Attention based Deep Convolutional Neural Network

no code implementations28 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.

Data Augmentation Environment Sound Classification +2

Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment

no code implementations23 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.

OpenAI Gym Q-Learning +3

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