7 papers
Scalable Bayesian Network Structure Learning Using Tsetlin Machine to Constrain the Search Space
Kunal Dumbre, Lei Jiao, Ole-Christoffer Granmo
The PC algorithm is a widely used method in causal inference for learning the structure of Bayesian networks. Despite its popularity, the PC algorithm suffers from significant time…
Uncertainty Quantification in the Tsetlin Machine
Runar Helin, Ole-Christoffer Granmo, Mayur Kishor Shende +5
Data modeling using Tsetlin machines (TMs) is all about building logical rules from the data features. The decisions of the model are based on a combination of these logical rules.…
Omni TM-AE: A Scalable and Interpretable Embedding Model Using the Full Tsetlin Machine State Space
Ahmed K. Kadhim, Lei Jiao, Rishad Shafik +1
The increasing complexity of large-scale language models has amplified concerns regarding their interpretability and reusability. While traditional embedding models like Word2Vec a…
An All-digital 8.6-nJ/Frame 65-nm Tsetlin Machine Image Classification Accelerator
Svein Anders Tunheim, Yujin Zheng, Lei Jiao +3
We present an all-digital programmable machine learning accelerator chip for image classification, underpinning on the Tsetlin machine (TM) principles. The TM is an emerging machin…
Scalable Multi-phase Word Embedding Using Conjunctive Propositional Clauses
Ahmed K. Kadhim, Lei Jiao, Rishad Shafik +2
The Tsetlin Machine (TM) architecture has recently demonstrated effectiveness in Machine Learning (ML), particularly within Natural Language Processing (NLP). It has been utilized…
Adversarial Attacks on AI-Generated Text Detection Models: A Token Probability-Based Approach Using Embeddings
Ahmed K. Kadhim, Lei Jiao, Rishad Shafik +1
In recent years, text generation tools utilizing Artificial Intelligence (AI) have occasionally been misused across various domains, such as generating student reports or creative…