The dataset facilitates two primary design paradigms: global, for creating complete architectures from scratch, and local, for detailed architecture component refinement, and contributes to a new frontier, Artificial Intelligence-Generated Neural Network Architecture (AIGNNA).

This paper introduces the discipline of evaluatology, which seeks to formalize the science and engineering of evaluation across various fields. It proposes a universal framework encompassing concepts, theories, and methodologies for evaluation, grounded in five key axioms. The paper also introduces the concept of benchmarkology, a universal approach to evaluation based on benchmark models that ensure different levels of equivalency.

The study unveils a counterintuitive revelation: deep learning inference quality exhibits fluctuations due to inference time, and proposes an initial evaluation framework to analyze factors affecting quality fluctuations, facilitating the prediction of the potential distribution of inference quality.

The dataset facilitates two primary design paradigms: global, for creating complete architectures from scratch, and local, for detailed architecture component refinement, and contributes to a new frontier, Artificial Intelligence-Generated Neural Network Architecture (AIGNNA).

It is the first post in my personal website.