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AI Institute: Institute for Foundations of Machine Learning

AI 研究所：机器学习基础研究所

基本信息

批准号：
2019844
负责人：
Adam Klivans
金额：
$ 2000万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Cooperative Agreement
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2019844&HistoricalAwards=false
关键词：
AI Institute Foundations Machine Learning

项目摘要

Machine Learning and AI have experienced unprecedented growth over the last ten years, especially on classical vision and language tasks. Expectations are high that the next decade of research will continue at the same pace, yielding transformative discoveries in nearly all areas of science. Complex machine perception tasks, however, still remain difficult. For example, tools for object recognition struggle with the dynamic nature and massive size of video data. What are the key foundational questions that need to be solved so that machine learning breaks through its current limitations? This institute (a partnership of The University of Texas at Austin, the University of Washington, Wichita State University, and Microsoft Research) will develop next-generation mathematical tools and algorithms with an eye towards major advances on core perceptual tasks. In the short term, principled heuristics will reduce the amount of trial and error that is prevalent in the current empirical framework. In the long term, new algorithms can reshape the landscape of machine learning with faster and more robust schemes for training and testing. Singular goals of this institute include integrated plans for transferring knowledge that links foundational progress with use-inspired research spanning video, imaging, and navigation. These plans address outstanding issues in municipalities (e.g., traffic mitigation), the technology sector (e.g., robust video delivery), and healthcare (e.g., medical imaging). This institute addresses the intense industrial demand for expertise in machine learning by developing a new online master’s degree program in AI that caters to working professionals with complex schedules. Undergraduates will be brought to the forefront of machine learning research through a sequence of new research initiatives across several majors including computer science, mathematics, electrical engineering, and statistics. The institute also focuses on new internal strategies to dramatically increase the number of women in AI as well as new enrichment programs in machine learning for educators and students in high schools to broaden participation in AI for underserved communities. The institute's research program identifies four key thrusts targeting major open problems in the foundations of machine learning: (i) Developing advanced algorithms for deep learning. The institute will create fast, provably efficient tools for training neural networks and searching parameter spaces. This includes formulating new analyses for gradient-based methods and applications to hyperparameter optimization and architecture search. (ii) Learning with dynamic data. Since datasets are constantly evolving, it is crucial to find algorithms and models that can incorporate changes at training and test time, including robustness to perturbations. A major emphasis will be on furthering a new field of efficient robust statistics, where the main objective is finding provably efficient algorithms for classical (often thought to be computationally intractable) statistical estimators. (iii) Exploiting Structure in Data. What characteristics of a dataset help with training and inference? Simple models involving sparsity often fail to capture modern training sets. This project will focus on more expressive ways to model the underlying structure in a distribution using deep networks as priors. Along these lines, the institute will investigate (both theoretically and experimentally) how structure can be used to address a fundamental mystery in supervised learning, namely why overparameterized networks generalize well when trained on real data. (iv) Optimizing real-world objectives. While black-box learning methods have improved by leaps and bounds in the recent past, it is difficult to use them in conjunction with complex loss functions that involve real-world constraints. For example, common vision tasks involve discriminating among different types of objects, but, depending on the downstream application, it may be more important to distinguish certain pairs of objects than others. For robot planning, mobility speed may be less important than adhering to certain safety constraints. This thrust will combine the current, optimization-based approach to machine learning with the expressive power of programming languages and formal methods. A related focus will be on imitation learning and interactive machine learning where objective functions can be adjusted based on real-world feedback from human users. The institute's four research thrusts dovetail with three use-inspired research projects in video, imaging, and navigation. These projects encapsulate frontier challenges in machine perception and provide a wealth of benchmarks to evaluate theoretical work. With respect to video, the institute will work with industrial partners to redesign the whole video pipeline: from recognition and compression/decompression to training and model design. For imaging, the focus will be on new algorithms to solve ill-posed inverse problems where novel priors can compensate for the loss of observed information, especially in the context of medical diagnostics. The navigation project will consider the algorithmic challenge of autonomous and safe transportation in highly unstructured environments and will address policy issues in coordination with local governments. Altogether, these solutions will expand the scope of machine learning by moving beyond a black-box model of prediction.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.

机器学习和人工智能在过去十年中经历了前所未有的增长，特别是在经典视觉和语言任务方面。人们对下一个十年的研究将以同样的速度继续下去寄予厚望，在几乎所有科学领域都产生变革性的发现。然而，复杂的机器感知任务仍然很困难。例如，用于对象识别的工具与视频数据的动态性质和大规模数据作斗争。机器学习需要解决哪些关键的基础问题，才能突破当前的限制？该研究所（由德克萨斯大学奥斯汀分校、华盛顿大学、威奇托州立大学和微软研究院合作）将开发下一代数学工具和算法，着眼于核心感知任务的重大进展。在短期内，有原则的经验主义将减少当前经验框架中普遍存在的试验和错误的数量。从长远来看，新算法可以通过更快、更强大的训练和测试方案重塑机器学习的格局。该研究所的独特目标包括转让知识的综合计划，将基础进展与跨视频，成像和导航的使用启发研究联系起来。这些计划解决了各城市的未决问题（例如，交通缓解），技术部门（例如，鲁棒的视频传送），以及医疗保健（例如，医学成像）。该研究所通过开发一个新的人工智能在线硕士学位课程来满足工业对机器学习专业知识的强烈需求，该课程旨在满足具有复杂时间表的专业人士的需求。本科生将通过一系列跨计算机科学、数学、电气工程和统计学等多个专业的新研究计划，被带到机器学习研究的前沿。该研究所还专注于新的内部战略，以大幅增加人工智能领域的女性人数，并为高中教育工作者和学生提供新的机器学习丰富计划，以扩大服务不足社区对人工智能的参与。该研究所的研究计划确定了针对机器学习基础中主要开放问题的四个关键目标：（i）开发深度学习的高级算法。该研究所将创建快速，可证明有效的工具来训练神经网络和搜索参数空间。这包括为基于梯度的方法制定新的分析，并将其应用于超参数优化和架构搜索。 (ii)学习动态数据。由于数据集在不断发展，因此找到能够在训练和测试时纳入变化（包括对扰动的鲁棒性）的算法和模型至关重要。一个主要的重点将是推进一个新的领域的有效的强大的统计，其中的主要目标是找到可证明的有效算法的经典（通常被认为是计算棘手）的统计估计。 (iii)利用数据中的结构。数据集的哪些特征有助于训练和推理？涉及稀疏性的简单模型通常无法捕获现代训练集。该项目将专注于更有表现力的方式，使用深度网络作为先验来建模分布中的底层结构。沿着这些路线，该研究所将研究（理论和实验）如何使用结构来解决监督学习中的一个基本谜团，即为什么过参数化网络在真实的数据上训练时能很好地泛化。(iv)优化现实世界的目标。虽然黑盒学习方法在最近的过去已经有了长足的进步，但很难将它们与涉及现实世界约束的复杂损失函数结合使用。例如，常见的视觉任务涉及区分不同类型的对象，但是，根据下游应用，区分某些对象对可能比其他对象对更重要。对于机器人规划，移动速度可能不如遵守某些安全约束重要。这将把当前基于优化的机器学习方法与编程语言和形式化方法的表达能力结合起来。一个相关的重点将是模仿学习和交互式机器学习，其中目标函数可以根据人类用户的真实反馈进行调整。该研究所的四个研究方向与视频、成像和导航领域的三个应用启发研究项目相吻合。这些项目涵盖了机器感知领域的前沿挑战，并提供了丰富的基准来评估理论工作。在视频方面，该研究所将与工业伙伴合作，重新设计整个视频管道：从识别和压缩/解压缩到培训和模型设计。对于成像，重点将是新的算法来解决不适定的逆问题，其中新的先验可以补偿观察到的信息的损失，特别是在医疗诊断的背景下。导航项目将考虑高度非结构化环境中自主和安全运输的算法挑战，并将与地方政府协调解决政策问题。总而言之，这些解决方案将超越黑箱预测模型，扩大机器学习的范围。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。