CAREER: Information-Theoretic and Statistical Foundations of Generative Models

职业：生成模型的信息理论和统计基础

基本信息

批准号：
1942230
负责人：
Soheil Feizi
金额：
$ 58.97万
依托单位：
University of Maryland, College Park
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-01 至 2025-02-28
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942230&HistoricalAwards=false
关键词：
CAREER Information Theoretic Statistical Foundations

项目摘要

Generative machine learning models provide a statistical understanding of data and play an important role in the success of modern machine learning in various application domains including vision, speech, natural languages, and computational biology, among others. Building on the success of deep learning, recent advances in modern generative models hold great promise in revolutionizing various learning methods. Despite this progress, the understanding of some fundamental aspects of these models, required for characterizing their performance guarantees, is still in its infancy. This project aims to elucidate statistical and computational properties of modern generative models by leveraging tools and concepts from information theory, statistics and optimization. This project also includes a comprehensive plan to integrate the research results into an inclusive, diverse and cross-disciplinary educational program at the high school, undergraduate and graduate levels. The overall goal of the research program is to develop a comprehensive and fundamental understanding of the intertwined statistical and computational aspects of modern generative models such as Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs). This project aims to make critical advances in proper formulations of generative models for high dimensional distributions, characterizing statistical limits of these models, and developing efficient computational approaches for solving optimization problems involved during their training. This cross-disciplinary project broadens the scope of the prior knowledge on the interplay between information theory and machine learning and creates a tightly connected loop between theory, algorithms and applications in data science.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.

生成的机器学习模型提供了对数据的统计理解，并在现代机器学习在各种应用领域的成功中起着重要作用，包括视觉，语音，自然语言和计算生物学等。基于深度学习的成功，现代生成模型的最新进展在革新各种学习方法方面具有巨大的希望。尽管取得了这种进步，但对这些模型的某些基本方面的理解仍处于起步阶段。该项目旨在通过利用信息理论，统计和优化的工具和概念来阐明现代生成模型的统计和计算特性。该项目还包括一项综合计划，将研究结果整合到高中，本科和研究生级别的包容性，多样化和跨学科的教育计划中。该研究计划的总体目标是对现代生成模型（例如生成对抗网络（GAN）（GAN））和变异自动编码器（VAES）等现代生成模型的统计和计算方面进行全面和基本的了解。该项目旨在在适当的生成模型中为高维分布的适当表述，表征这些模型的统计限制，并开发有效的计算方法，以解决培训期间涉及的优化问题。这个跨学科的项目扩大了有关信息理论与机器学习之间相互作用的先验知识的范围，并在数据科学中的理论，算法和应用之间建立了紧密相关的环节。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子的知识和宽广的影响来通过评估来评估的，并被认为是值得的。