Announcement: Revised Edition of the Technical Note Published on Zenodo A revised edition of the technical note “A Construction Method for Neural Networks Without Activation Functions” has been published on Zenodo. This updated version includes a newly added Appendix, which provides a comprehensive guide to a series of articles documenting practical implementations of the Tenda Categorization Network (TCN). These resources illustrate TCN’s applications in supervised learning, unsupervised learning, reinforcement learning, anomaly detection, hierarchical decision systems, and more. Importantly, the Appendix highlights how TCN functions as a language reasoning engine, offering a potential solution to the structural limitations inherent in current Large Language Models (LLMs). It is hoped that this revised edition will support deeper understanding and further research on TCN as a promising architecture for safe, interpretable, and structurally grounded AI.
Deep Neural Networks (DNNs) exhibit acute vulnerabilities to intermediate activation layer perturbations engineered through out-of-distribution (OOD) noise injection and feature-steering gradient updates. Conventional defensive paradigms—such as adversarial retraining or external…
Abstract: 5G is rolled out and next generation 6G networks are also being developed, ultra-low latency (URLL) communication as a standard is critical in supporting the plethora of applications, spanning autonomous vehicles, immersive extended reality experience, etc. However, tra…
Neural networks are extraordinarily effective and almost entirely opaque: their competence is real but unreadable, and adapting them means retraining usually via a data-center process, not something that happens in the moment, in context. Symbolic systems are the inverse, legible…