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

CDS

• 批准号: 2312173
• 负责人: Rui Tuo
• 金额: $ 36万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312173&HistoricalAwards=false
• 关键词: CDS&E; MSS; Sparsely; Activated; Bayesian

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.

该数学和统计科学（CDS＆E-MSS）项目中的计算和数据支持的科学与工程旨在开发一种系统的方法，用于构建深层贝叶斯神经网络，既有计算上有效且可用于模型设计。预计该项目的结果将导致与当前机器学习和人工智能模型的准确性，信心和效率有关。开源软件将在此项目过程中开发，为研究人员和从业人员提供可访问的工具。预计这项研究将影响广泛的应用，包括自动驾驶汽车，医疗诊断以及贸易和金融。研究人员还致力于为少数群体和代表性不足的群体促进STEM教育和研究机会，该项目还为研究生提供了研究培训机会。预计该研究将在几个关键领域中推进高斯流程模型和相关的贝叶斯神经网络，包括理论和算法开发，它们在生成任务中的应用以及软件开发和验证。研究和开发计划主要由以下三个组成部分组成：1）更好地了解深张量Markov Gaussian过程模型的性质，并构建具有更复杂体系结构的更广泛的模型，但仍然可以通过稀疏结构的深贝叶斯神经网络近似； 2）探索深高斯工艺模型的随机性，以创建新颖的条件生成模型； 3）为开发软件工具并使用现实世界中的无线沟通和计算机视觉系统的现实数据集验证项目成果。这是NSF数学科学部颁发的奖项，由民用，机械和制造创新的部门共同支持。这些奖项通过评估了NSF的范围，其范围是由NSF的范围进行了评估。 标准。