Search Results for author: Lei Jiao

Found 27 papers, 14 papers with code

ConvTextTM: An Explainable Convolutional Tsetlin Machine Framework for Text Classification

no code implementations LREC 2022 Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao

Recent advancements in natural language processing (NLP) have reshaped the industry, with powerful language models such as GPT-3 achieving superhuman performance on various tasks.

Decision Making Document Classification +2

Contracting Tsetlin Machine with Absorbing Automata

no code implementations17 Oct 2023 Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao, Per-Arne Andersen, Svein Anders Tunheim, Rishad Shafik, Alex Yakovlev

In brief, the TA of each clause literal has both an absorbing Exclude- and an absorbing Include state, making the learning scheme absorbing instead of ergodic.

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

Learning Minimalistic Tsetlin Machine Clauses with Markov Boundary-Guided Pruning

1 code implementation12 Sep 2023 Ole-Christoffer Granmo, Per-Arne Andersen, Lei Jiao, Xuan Zhang, Christian Blakely, Tor Tveit

A set of variables is the Markov blanket of a random variable if it contains all the information needed for predicting the variable.

Bayesian Inference

When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Framework

no code implementations22 May 2023 Xinjing Yuan, Lingjun Pu, Lei Jiao, Xiaofei Wang, Meijuan Yang, Jingdong Xu

In this paper, we advocate CPN-FedSL, a novel and flexible Federated Split Learning (FedSL) framework over Computing Power Network (CPN).


Interpretable Tsetlin Machine-based Premature Ventricular Contraction Identification

no code implementations20 Jan 2023 Jinbao Zhang, Xuan Zhang, Lei Jiao, Ole-Christoffer Granmo, Yongjun Qian, Fan Pan

In this study, we develop a Tsetlin machine (TM) based architecture for premature ventricular contraction (PVC) identification by analysing long-term ECG signals.

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.

Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook

no code implementations29 Sep 2021 Morten Goodwin, Kim Tallaksen Halvorsen, Lei Jiao, Kristian Muri Knausgård, Angela Helen Martin, Marta Moyano, Rebekah A. Oomen, Jeppe Have Rasmussen, Tonje Knutsen Sørdalen, Susanna Huneide Thorbjørnsen

We provide insight into popular deep learning approaches for ecological data analysis in plain language, focusing on the techniques of supervised learning with deep neural networks, and illustrate challenges and opportunities through established and emerging applications of deep learning to marine ecology.

Management object-detection +1

On the Convergence of Tsetlin Machines for the AND and the OR Operators

2 code implementations17 Sep 2021 Lei Jiao, Xuan Zhang, Ole-Christoffer Granmo

The analyses on AND and OR operators, together with the previously analysed 1-bit and XOR operations, complete the convergence analyses on basic operators in Boolean algebra.

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

On the Convergence of Tsetlin Machines for the XOR Operator

6 code implementations7 Jan 2021 Lei Jiao, Xuan Zhang, Ole-Christoffer Granmo, K. Darshana Abeyrathna

The Tsetlin Machine (TM) is a novel machine learning algorithm with several distinct properties, including transparent inference and learning using hardware-near building blocks.

Measuring the Novelty of Natural Language Text Using the Conjunctive Clauses of a Tsetlin Machine Text Classifier

5 code implementations17 Nov 2020 Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao

The mechanism uses the conjunctive clauses of the TM to measure to what degree a text matches the classes covered by the training data.

Novelty Detection Text Classification

Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling

2 code implementations10 Sep 2020 K. Darshana Abeyrathna, Bimal Bhattarai, Morten Goodwin, Saeed Gorji, Ole-Christoffer Granmo, Lei Jiao, Rupsa Saha, Rohan K. Yadav

We evaluated the proposed parallelization across diverse learning tasks and it turns out that our decentralized TM learning algorithm copes well with working on outdated data, resulting in no significant loss in learning accuracy.

On the Convergence of Tsetlin Machines for the IDENTITY- and NOT Operators

no code implementations28 Jul 2020 Xuan Zhang, Lei Jiao, Ole-Christoffer Granmo, Morten Goodwin

The analysis of the convergence of the two basic operators lays the foundation for analyzing other logical operators.

Operator learning

The Convolutional Tsetlin Machine

8 code implementations arXiv 2019 Ole-Christoffer Granmo, Sondre Glimsdal, Lei Jiao, Morten Goodwin, Christian W. Omlin, Geir Thore Berge

Whereas the TM categorizes an image by employing each clause once to the whole image, the CTM uses each clause as a convolution filter.

Image Classification

The Regression Tsetlin Machine: A Tsetlin Machine for Continuous Output Problems

1 code implementation10 May 2019 K. Darshana Abeyrathna, Ole-Christoffer Granmo, Lei Jiao, Morten Goodwin

We achieve this by: (1) using the conjunctive clauses of the TM to capture arbitrarily complex patterns; (2) mapping these patterns to a continuous output through a novel voting and normalization mechanism; and (3) employing a feedback scheme that updates the TM clauses to minimize the regression error.

General Classification regression

Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications

1 code implementation12 Sep 2018 Geir Thore Berge, Ole-Christoffer Granmo, Tor Oddbjørn Tveit, Morten Goodwin, Lei Jiao, Bernt Viggo Matheussen

The Tsetlin Machine either performs on par with or outperforms all of the evaluated methods on both the 20 Newsgroups and IMDb datasets, as well as on a non-public clinical dataset.

Natural Language Understanding Text Categorization

Cannot find the paper you are looking for? You can Submit a new open access paper.