Modern deep learning tools are remarkably effective in addressing intricate problems. However, their operation as black-box models introduces increased uncertainty in predictions. Additionally, they contend with various challenges, including the need for substantial storage space in large networks, issues of overfitting, underfitting, vanishing gradients, and more. This study explores the concept of Bayesian Neural Networks, presenting a novel architecture designed to significantly alleviate the storage space complexity of a network. Furthermore, we introduce an algorithm adept at efficiently handling uncertainties, ensuring robust convergence values without becoming trapped in local optima, particularly when the objective function lacks perfect convexity.

翻译：现代深度学习工具在处理复杂问题方面表现卓越，然而其作为黑箱模型的特性却增加了预测的不确定性。此外，这些模型还面临诸多挑战，包括大型网络需占用大量存储空间、过拟合与欠拟合问题、梯度消失现象等。本研究深入探讨贝叶斯神经网络的概念，提出一种新型架构，旨在大幅降低网络的存储空间复杂度。同时，我们引入了一种算法，该算法能高效处理不确定性，确保在不完全凸目标函数的条件下，实现稳健的收敛值而避免陷入局部最优解。