CCF: Medium: Inference with dynamic deep probabilistic models

CCF：中：使用动态深度概率模型进行推理

• 批准号: 2212506
• 负责人: Petar Djuric
• 金额: $ 119.93万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2212506&HistoricalAwards=false
• 关键词: CCF; Medium; Inference; dynamic; deep

The dynamics of complex systems are often studied by processing multivariate time signals that are produced by these systems. Improved understanding of the systems from such signals hinges on working with accurate models of the systems. The rationale of the proposed methodology for making inference from multivariate time signals stands on three important principles: algorithmic compressibility, locality, and deep probabilistic modeling. With algorithmic compressibility, one interprets seemingly complex high-dimensional data in much lower dimensional spaces. With locality, one exploits the fact that in nature the most influential events to an event are its local events. With deep probabilistic modeling, one aims at finding algorithmic compressibilities. These principles are used for developing novel models with little prior knowledge about the dynamics of the observed system. Another challenging problem of interest in the project is discovering causes and effects based on the adopted models. The developed methods are tested on multivariate local field potentials acquired from patients with epilepsy. Based on these signals, the objective is to find the zones in the brain that cause seizures in the patients. Finding these zones and removing them by surgery often cures the patients. The project conceptualizes a principled approach to building deep state-space models with deep probabilistic modeling. The research includes the development of theory and methods for estimating the unknowns of these models, investigation of methods for estimating the structures of the models, extension of the new methods to models that capture regime switching, development of theory and methods for discovering causalities among multivariate time signals, and identification of states that cause seizures in patients with epilepsy. The research is based on minimal assumptions about the models and is carried out within the Bayesian framework. The methodology is not data hungry, and all the produced results are probabilistic in nature. This research on deep probabilistic models and causal discovery considerably extends the capabilities for modeling multivariate time signals, which not only facilitates our understanding of complex systems but also offers new paradigms that extend the horizons and scope of signal processing and machine learning. The applications in medicine and neurosurgery, such as identifying the pathological zones in the brain of epilepsy patients that cause seizures stand on their merit.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.

通常通过处理这些系统产生的多元时间信号来研究复杂系统的动力学。从此类信号中提高了对系统的理解，取决于与系统的准确模型合作。从多元时间信号进行推断的提出方法的基本原理基于三个重要原则：算法可压缩性，位置和深层概率建模。使用算法可压缩性，人们在较低的维空间中解释了看似复杂的高维数据。有了地区，人们利用了这样一个事实，即事件最有影响力的事件是其当地事件。有了深层的概率建模，一个旨在寻找算法可压缩性。这些原理用于开发新型模型，几乎没有关于观察到系统动力学的知识。该项目的另一个具有挑战性的问题是根据所采用的模型发现原因和效果。对从癫痫患者获得的多元局部现场电位进行了测试。基于这些信号，目的是找到大脑中引起患者癫痫发作的区域。找到这些区域并通过手术去除它们，经常治愈患者。 该项目概念化了一种有原则的方法，可以通过深层概率建模来构建深层的状态空间模型。该研究包括开发理论和方法，用于估计这些模型未知数，研究估算模型结构的方法，将新方法扩展到捕获政权转换的模型，理论的发展和发现多元时间信号中因果关系的方法，以及在癫痫患者中引起癫痫发作的状态的鉴定。该研究基于对模型的最小假设，并在贝叶斯框架内进行。该方法不是饥饿的数据，所有产生的结果本质上都是概率的。 这项对深层概率模型和因果发现的研究大大扩展了建模多元时间信号的功能，这不仅促进了我们对复杂系统的理解，而且提供了扩展信号处理和机器学习范围的新范式。在医学和神经外科手术中的应用，例如识别引起癫痫发作的癫痫患者大脑中的病理区域。该奖项反映了NSF的法定任务，并被认为值得通过基金会的知识分子优点和更广泛的影响标准通过评估来进行评估。

项目成果

A Gaussian Latent Variable Model for Incomplete Mixed Type Data

• DOI: 10.1109/icassp49357.2023.10095772
• 发表时间: 2023-06
• 期刊: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: Marzieh Ajirak;P. Djurić

An approach to learning the hierarchical organization of the frontal lobe

学习额叶层次结构的方法

• 发表时间: 2023
• 期刊: EUSIPCO
• 作者: K. Butler, D. Cleveland

A Differential Measure of the Strength of Causation

因果关系强度的差异测量

• DOI: 10.1109/lsp.2022.3215917
• 发表时间: 2022
• 期刊: IEEE Signal Processing Letters
• 影响因子: 3.9
• 作者: Butler, Kurt;Feng, Guanchao;Djuric, Petar M.
• 通讯作者: Djuric, Petar M.

Estimation of time-varying graph topologies from graph signals

从图信号估计时变图拓扑

• 发表时间: 2023
• 期刊: Conference record IEEE International Conference on Acoustics Speech and Signal Processing
• 作者: Y. Liu, C. Cui

On Causal Discovery With Convergent Cross Mapping

• DOI: 10.1109/tsp.2023.3286529
• 发表时间: 2023
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Kurt Butler;Guanchao Feng;P. Djurić