3 papers
cs.LG2025
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…
cs.LG2025
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…
cs.CL2025
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…