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展现出显著效果。然而，它们作为黑箱模型运行时会增加预测的不确定性。此外，它们还面临各种挑战，包括大型网络需要大量存储空间、过拟合、欠拟合、梯度消失等问题。本研究探讨了贝叶斯神经网络的概念，提出了一种新型架构，旨在显著降低网络的存储空间复杂度。此外，我们引入了一种能够高效处理不确定性的算法，确保在目标函数不具备完美凸性时，也能获得鲁棒的收敛值，而不会陷入局部最优。