Facilitating Experimental Reproducibility in Neural Network Research with a Unified Framework
Anestis Kaimakamidis, Ioannis Pitas · 2023
In the realm of neural network research, achieving experiment reproducibility is paramount for building upon existing knowledge and advancing the field. This paper examines a multi-agent neural network framework on its ability to facilitate the reproduction of experiments. Also, we address the reproducibility problem when there are data or source code limitations. The framework offers crucial functionalities for facilitating experiment reproducibility achieved through data, layer outputs, architectures, and weights exchange among the framework's agents. Through the integration of these functionalities, this framework empowers researchers to reproduce and validate experimental results consistently, fostering a more robust and collaborative research environment in the field of neural networks. The experimental results demonstrate the framework's reproducibility abilities. Furthermore, we test the framework in terms of reproducibility in an emergency natural disaster management situation. Finally, we analyze how the privacy limitations of the original neural network affect the reproducibility results.