CDS&E-MSS: Sparsely Activated Bayesian Neural Networks from Deep Gaussian Processes

该项目的结果预计将导致在准确性、置信度和效率方面比目前的机器学习和人工智能模型有显著改进。将在该项目过程中开发开放源码软件，为研究人员和从业人员提供可用的工具。这项研究预计将影响包括自动驾驶汽车、医疗诊断以及贸易和金融在内的广泛应用。调查人员还致力于促进少数族裔和代表性不足群体的STEM教育和研究机会，该项目还为研究生提供研究培训机会。这项研究有望在几个关键领域推进深度高斯过程模型和相关的贝叶斯神经网络，包括理论和算法的开发，它们在生成性任务中的应用，以及软件开发和验证。研究和开发计划主要包括以下三个部分：1)更好地了解深度张量马尔可夫-高斯过程模型的性质，构造更广泛的具有更复杂结构但仍然可以被稀疏结构的深度贝叶斯神经网络逼近的模型：2)探索深度张量马尔可夫-高斯过程模型的随机性质，创建新的条件生成模型；3)开发软件工具，并使用来自最先进的无线通信和计算机视觉系统的真实世界数据集来验证项目成果。该奖项由NSF数学科学部联合支持，由土木工程、机械和制造创新部共同支持。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This Computational and Data-Enabled Science and Engineering in Mathematical and Statistical Sciences (CDS&E-MSS) project aims to develop a systematic approach for constructing deep Bayesian neural networks that are both computationally efficient and amenable to model designs. The results of this project are expected to lead to significant improvements in terms of accuracy, confidence, and efficiency over the current machine learning and artificial intelligence models. Open-source software will be developed during the course of this project, providing accessible tools for researchers and practitioners. This study is expected to impact a wide range of applications including self-driving cars, medical diagnostics, and trading and finance. The investigators are also committed to promoting STEM education and research opportunities for minority and underrepresented groups, and this project also provides research training opportunities for graduate students. The research is expected to advance deep Gaussian process models and relevant Bayesian neural networks in several key areas, including theory and algorithmic development, their applications to generative tasks, and software development and validation. The research and development plan mainly consists of the following three components: 1) to better understand the properties of deep tensor Markov Gaussian process models and to construct a broader class of models that have more complex architectures but can still be approximated by deep Bayesian neural networks with sparse structure; 2) to explore the stochastic nature of deep Gaussian process models to create novel conditional generative models; and 3) to develop software tools and to validate the project outcomes using real-world datasets from the state-of-the-art wireless communication and computer vision systems.This award by the NSF Division of Mathematical Sciences is jointly supported by the Division of Civil, Mechanical, and Manufacturing Innovation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.