47 citations · 150 across the 17 of their papers we have counts for
18 papers
Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook
Morten Goodwin, Kim Tallaksen Halvorsen, Lei Jiao +7
The deep learning revolution is touching all scientific disciplines and corners of our lives as a means of harnessing the power of big data. Marine ecology is no exception. These n…
On the Convergence of Tsetlin Machines for the AND and the OR Operators
Lei Jiao, Xuan Zhang, Ole-Christoffer Granmo
The Tsetlin Machine (TM) is a novel machine-learning algorithm based on propositional logic, which has obtained state-of-the-art performance on several pattern recognition problems…
Explainable Tsetlin Machine framework for fake news detection with credibility score assessment
Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao
The proliferation of fake news, i.e., news intentionally spread for misinformation, poses a threat to individuals and society. Despite various fact-checking websites such as Politi…
Word-level Human Interpretable Scoring Mechanism for Novel Text Detection Using Tsetlin Machines
Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao
Recent research in novelty detection focuses mainly on document-level classification, employing deep neural networks (DNN). However, the black-box nature of DNNs makes it difficult…
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…
Enhancing Interpretable Clauses Semantically using Pretrained Word Representation
Rohan Kumar Yadav, Lei Jiao, Ole-Christoffer Granmo +1
Tsetlin Machine (TM) is an interpretable pattern recognition algorithm based on propositional logic, which has demonstrated competitive performance in many Natural Language Process…
Arena-Rosnav: Towards Deployment of Deep-Reinforcement-Learning-Based Obstacle Avoidance into Conventional Autonomous Navigation Systems
Linh Kästner, Teham Buiyan, Xinlin Zhao +3
Recently, mobile robots have become important tools in various industries, especially in logistics. Deep reinforcement learning emerged as an alternative planning method to replace…
On the Convergence of Tsetlin Machines for the XOR Operator
Lei Jiao, Xuan Zhang, Ole-Christoffer Granmo +1
The Tsetlin Machine (TM) is a novel machine learning algorithm with several distinct properties, including transparent inference and learning using hardware-near building blocks. A…
A Comprehensive Utility Function for Resource Allocation in Mobile Edge Computing
Zaiwar Ali, Sadia Khaf, Ziaul Haq Abba +5
In mobile edge computing (MEC), one of the important challenges is how much resources of which mobile edge server (MES) should be allocated to which user equipment (UE). The existi…
Measuring the Novelty of Natural Language Text Using the Conjunctive Clauses of a Tsetlin Machine Text Classifier
Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao
Most supervised text classification approaches assume a closed world, counting on all classes being present in the data at training time. This assumption can lead to unpredictable…
Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling
K. Darshana Abeyrathna, Bimal Bhattarai, Morten Goodwin +5
Using logical clauses to represent patterns, Tsetlin Machines (TMs) have recently obtained competitive performance in terms of accuracy, memory footprint, energy, and learning spee…
On the Convergence of Tsetlin Machines for the IDENTITY- and NOT Operators
Xuan Zhang, Lei Jiao, Ole-Christoffer Granmo +1
The Tsetlin Machine (TM) is a recent machine learning algorithm with several distinct properties, such as interpretability, simplicity, and hardware-friendliness. Although numerous…
Temperate Fish Detection and Classification: a Deep Learning based Approach
Kristian Muri Knausgård, Arne Wiklund, Tonje Knutsen Sørdalen +4
A wide range of applications in marine ecology extensively uses underwater cameras. Still, to efficiently process the vast amount of data generated, we need to develop tools that c…
The Regression Tsetlin Machine: A Tsetlin Machine for Continuous Output Problems
K. Darshana Abeyrathna, Ole-Christoffer Granmo, Lei Jiao +1
The recently introduced Tsetlin Machine (TM) has provided competitive pattern classification accuracy in several benchmarks, composing patterns with easy-to-interpret conjunctive c…
The Convolutional Tsetlin Machine
Ole-Christoffer Granmo, Sondre Glimsdal, Lei Jiao +3
Convolutional neural networks (CNNs) have obtained astounding successes for important pattern recognition tasks, but they suffer from high computational complexity and the lack of…
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation
Erlend Olsvik, Christian M. D. Trinh, Kristian Muri Knausgård +5
Our understanding and ability to effectively monitor and manage coastal ecosystems are severely limited by observation methods. Automatic recognition of species in natural environm…
Learning Automata Based Q-learning for Content Placement in Cooperative Caching
Zhong Yang, Yuanwei Liu, Yue Chen +1
An optimization problem of content placement in cooperative caching is formulated, with the aim of maximizing sum mean opinion score (MOS) of mobile users. Firstly, a supervised fe…
Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications
Geir Thore Berge, Ole-Christoffer Granmo, Tor Oddbjørn Tveit +3
Medical applications challenge today's text categorization techniques by demanding both high accuracy and ease-of-interpretation. Although deep learning has provided a leap ahead i…