Watermarking and Forensics for AI Models, Data, and Deep Neural Networks
In my previous paper posted here, I explained how I built a new class of non-standard deep neural networks, with various case studies based on synthetic data and open-source code, covering problems such as noise filtering, high-dimensional curve fitting, and predictive analytics. One of the models featured a promising universal function able to represent any type of smooth response, while leading to fast convergence. Here I explore weight sensitivity and distillation. The words weight and parameter are used […]