The emerging paradigm of Tsetlin machines makes a fundamental shift from arithmetic-based to logic-based machine learning. At the core, finite-state machines, based on learning automata, learn patterns using logical clauses, and these constitute a global description of the task learnt. In this way, the Tsetlin machine introduces the concept of logical interpretable learning, where both the learned model and the process of learning are easy to follow and explain. As a result, it reduces the expertise needed to apply ML techniques efficiently in various domains. The paradigm has enabled competitive accuracy, scalability, memory footprint, inference speed, and energy consumption across diverse tasks, including classification, convolution, regression, natural language processing (NLP), and speech understanding.
Topics of Interest
Topics of interest include (but are not limited to):
- Tsetlin Machines
- Learning Automata
- Novel AI Algorithms
- Explainable and Interpretable AI
- Energy-efficient AI Systems Design
- New AI Applications, including Signal- and Image Processing
- Intelligent Data Preprocessing
Important Dates
- Paper Submission deadline: March 4, 2022
- Notification to authors: April 22, 2022
- Camera-ready submission deadline: May 20, 2022
This articles was originally published on the 20th of February, 2022.