The field of machine learning has seen tremendous progress in recent years,
with deep learning models delivering exceptional performance across a range of
tasks. However, these models often come at the cost of interpretability, as
they operate as opaque "black boxes" that obscure the rationale behind their
decisions. This lack of transparency can limit understanding of the models'
underlying principles and impede their deployment in sensitive domains, such as
healthcare or finance. To address this challenge, our research team has
proposed an innovative framework designed to reconcile the trade-off between
model performance and interpretability. Our approach is centered around modular
operations on high-dimensional data, which enable end-to-end processing while
preserving interpretability. By fusing diverse interpretability techniques and
modularized data processing, our framework sheds light on the decision-making
processes of complex models without compromising their performance. We have
extensively tested our framework and validated its superior efficacy in
achieving a harmonious balance between computational efficiency and
interpretability. Our approach addresses a critical need in contemporary
machine learning applications by providing unprecedented insights into the
inner workings of complex models, fostering trust, transparency, and
accountability in their deployment across diverse domains.

机器学习研究领域近年来取得巨大进展，深度学习模型在各种任务上表现出色。然而，这些模型往往在可解释性方面存在不足，因为它们作为不透明的 “黑盒子” 运行，使其决策背后的原理被遮蔽。为了解决这一挑战，我们的研究团队提出了一个创新框架，旨在权衡模型性能和可解释性之间的折衷。我们的方法以对高维数据的模块化操作为核心，可以进行端到端处理同时保持可解释性。通过融合多样化的解释技术和模块化数据处理，我们的框架可以揭示复杂模型的决策过程，而不损害其性能。我们广泛测试了我们的框架，并验证了其在实现计算效率和可解释性之间取得协调平衡方面的卓越成效。我们的方法通过在各个领域的部署中提供前所未有的对复杂模型内部工作原理的洞察，促进信任、透明度和问责制，满足当代机器学习应用的关键需求。