CAREER: Foundations of Small Data

职业：小数据的基础

• 批准号: 2145164
• 负责人: Pratik Chaudhari
• 金额: $ 54.91万
• 依托单位: University of Pennsylvania
• 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: 2022-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2145164&HistoricalAwards=false
• 关键词: CAREER; Foundations; Small; Data

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Deep Learning is a technique where one builds an artificial neural network that mimics the working of neurons in the biological brain. This technique drives a wide range of tasks today, e.g., predicting the next word while typing on the phone, tagging photos with the names of people in it, transcribing speech, etc. Building the artificial network requires collecting a large amount of data from each of these tasks. But as we seek to apply deep learning to more and more diverse tasks, it is becoming difficult to collect such large amounts of data from every task. For example, a number of languages or dialects have much fewer speakers than English or Spanish, and so their data is more scarce. This data scarcity is even more acute in domains such as the clinical sciences. The goal of this project is to develop theoretical and computational tools that enable artificial neural networks to work well even with few data. Educational and outreach goals of this project include (a) development of new curricula for graduate and undergraduate students, (b) mentoring trainees who work across established disciplines such as computer science, physics and engineering, and (c) fostering an ecosystem for machine learning across high-schools, higher-educational institutions and industry in the Greater Philadelphia region.In order to achieve these goals, this project will develop a foundational understanding of learning tasks. It will study how typical learning tasks have a certain effective low-dimensional structure that enables deep networks to learn such tasks efficiently. It seeks to characterize the geometry of the function space of predictive models fitted on typical tasks to understand when learning one task helps, or does not help, reduce the amount of data required to learn another task. It aims to exploit this geometry to build Bayesian priors that automatically adapt to the amount of available data. It is expected that such methods will reduce the amount of labeled data required for training by up to 1000 times. This theory will be used to develop new methods for transfer, multi-task and continual learning, and tools that enable accurate diagnosis of Alzheimer’s Disease.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.

该奖项的全部或部分资金来自2021年美国救援计划法案(公法117-2)。深度学习是一种技术，通过这种技术，人们可以建立一个人工神经网络，模仿生物大脑中神经元的工作。这项技术推动了当今广泛的任务，例如，在手机上打字时预测下一个单词，在照片上标记人名，转录语音等等。建立人工网络需要从每一项任务中收集大量数据。但随着我们寻求将深度学习应用于越来越多样化的任务，从每项任务中收集如此大量的数据变得越来越困难。例如，许多语言或方言的使用者比英语或西班牙语少得多，因此他们的数据更稀缺。这种数据稀缺在临床科学等领域更加严重。该项目的目标是开发理论和计算工具，使人工神经网络即使在数据很少的情况下也能很好地工作。该项目的教育和推广目标包括(A)为研究生和本科生开发新的课程，(B)指导跨计算机科学、物理和工程等现有学科工作的受训人员，以及(C)在大费城地区的高中、高等教育机构和行业中培养机器学习的生态系统。为了实现这些目标，该项目将发展对学习任务的基础理解。它将研究典型的学习任务如何具有某种有效的低维结构，使深度网络能够有效地学习这些任务。它试图表征适用于典型任务的预测模型的函数空间的几何形状，以理解何时学习一个任务有助于或无助于减少学习另一个任务所需的数据量。它的目标是利用这种几何学来建立自动适应可用数据量的贝叶斯先验。预计这种方法将把训练所需的标记数据量减少多达1000倍。这一理论将被用来开发转移、多任务和持续学习的新方法，以及能够准确诊断阿尔茨海默病的工具。这一奖项反映了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。