BIGDATA: F: Compositional Learning, Maps and Transfer: Statistical and Machine Learning on Collections of Data Sets

BIGDATA：F：组合学习、地图和迁移：数据集集合的统计和机器学习

基本信息

批准号：
1837991
负责人：
Mauro Maggioni
金额：
$ 70万
依托单位：
Johns Hopkins University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-01-01 至 2022-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1837991&HistoricalAwards=false
关键词：
BIGDATA Compositional Learning Maps Transfer

项目摘要

One of the landmarks of human intelligence is the ability to not only find solutions to hard problems, but to learn from past experiences and accumulate knowledge that may be (partially) transferred for quickly solving new problems. This project will develop novel foundational techniques for learning compositional rules, from collections of data sets and machine learning problems. The building blocks that the investigator will develop enable sharing of learning across multiple data sets and modalities. A first building block will enable machine learning algorithms to store solutions to past problems and use maps and abstractions to transfer knowledge to new problems. This requires efficient techniques for learning maps, how to compose them to enable knowledge transfer, all in a way that is compatible with the representation of the problems and their solutions, which also need to be automatically learned. These ideas will be tested on problems ranging from object and pattern recognition of images to behavior of interacting agent systems, from fusing data sets acquired with different sensors to controlling virtual and real agents. This project will provide general, foundational results in machine learning, which can be applied to applications in virtually any domain of human endeavor. The investigator will develop new techniques focused on representation and transfer learning, in particular: (i) Compositional Learning: the ability to learn and factorize through composition maps between data sets, and of functions (for classification and regression tasks) on data sets (e.g. the task f may be learned by using the map h to one data set on which learning already occurred and the already-learned function g on that data), in order to enhance both learning rates, knowledge extraction and transfer across data sets and data types; (ii) Map Learning: the ability to efficiently learn, represent, store, recall and apply maps between complex data sets, possibly of different modalities; but also learn maps that transform, at least approximately, one task into another, and transfer knowledge from one task to another; (iii) Representation Learning: the ability to learn how to efficiently represent, store and recall complex data sets, across multiple sensor modalities, and across different levels of abstractions -- for example, learning efficient representations of data from multiple types of sensors, learning of classifiers and regression functions, or learning interaction kernels in agent-based systems, as well as transfer those functions across sensor modalities, data sets, dynamical systems. While advancing current state of art techniques in each of these learning abilities, the research will tackle applications in learning invariances and performing object recognition tasks in images, detecting whether objects in an image are new or known, learn interaction rules from observing trajectories of interacting agent systems, and implement the ideas of compositional learning in the context of learning systems both virtual (for examples, using the OpenAI challenges) and real (for example, using robots), on sequences of tasks of increasing difficulty.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.

人类智能的里程碑之一是不仅能够找到难题的解决方案，而且能够从过去的经验中学习并积累可以（部分）转移以快速解决新问题的知识。该项目将开发新颖的基础技术，用于从数据集和机器学习问题的集合中学习组合规则。研究者将开发的构建模块能够跨多个数据集和模式共享学习。第一个构建块将使机器学习算法能够存储过去问题的解决方案，并使用映射和抽象将知识转移到新问题。这需要有效的学习地图技术，以及如何组合它们以实现知识迁移，所有这些都以与问题及其解决方案的表示兼容的方式进行，这些也需要自动学习。这些想法将在从图像的对象和模式识别到交互代理系统的行为、从融合使用不同传感器获取的数据集到控制虚拟和真实代理等问题上进行测试。该项目将提供机器学习的一般性、基础性结果，可应用于人类活动的几乎任何领域的应用。研究者将开发专注于表示和迁移学习的新技术，特别是：（i）组合学习：通过数据集之间的组合图以及数据集上的函数（用于分类和回归任务）进行学习和分解的能力（例如，任务 f 可以通过使用已经发生学习的一个数据集的映射 h 以及该数据上已经学习的函数 g 来学习，以提高学习率、知识提取以及跨数据集和数据类型的迁移; (ii) 地图学习：有效学习、表示、存储、回忆和应用可能具有不同模式的复杂数据集之间的地图的能力；还可以学习至少近似地将一项任务转换为另一项任务的地图，并将知识从一项任务转移到另一项任务； (iii) 表示学习：学习如何跨多种传感器模式、跨不同抽象层次有效表示、存储和调用复杂数据集的能力——例如，学习来自多种类型传感器的数据的有效表示、学习分类器和回归函数，或学习基于代理的系统中的交互内核，以及跨传感器模式、数据集、动态系统传输这些函数。在推进每种学习能力中当前最先进技术的同时，该研究将解决学习不变性和在图像中执行对象识别任务的应用，检测图像中的对象是新的还是已知的，通过观察交互代理的轨迹来学习交互规则系统，并在虚拟（例如，使用 OpenAI 挑战）和真实（例如，使用机器人）学习系统的背景下，针对难度不断增加的任务序列，实施组合学习的思想。该奖项反映了 NSF 的法定使命并被认为值得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来获得支持。

项目成果

期刊论文数量（12）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Learning by Active Nonlinear Diffusion

DOI：
10.3934/fods.2019012
发表时间：
2019-05
期刊：
ArXiv
影响因子：
0
作者：
M. Maggioni;James M. Murphy
通讯作者：
M. Maggioni;James M. Murphy

Learning Interaction Kernels in Stochastic Systems of Interacting Particles from Multiple Trajectories

学习多轨迹相互作用粒子随机系统中的相互作用核

DOI：
10.1007/s10208-021-09521-z
发表时间：
2021
期刊：
Foundations of Computational Mathematics
影响因子：
3
作者：
Lu, Fei;Maggioni, Mauro;Tang, Sui
通讯作者：
Tang, Sui

Learning Interaction Kernels for Agent Systems on Riemannian Manifolds

学习黎曼流形上代理系统的交互内核

DOI：
发表时间：
2021
期刊：
Proceedings of Machine Learning Research
影响因子：
0
作者：
Mauro Maggioni, Jason J
通讯作者：
Mauro Maggioni, Jason J

Nonparametric inference of interaction laws in systems of agents from trajectory data

从轨迹数据中非参数推断智能体系统中的相互作用规律

DOI：
10.1073/pnas.1822012116
发表时间：
2019
期刊：
Proceedings of the National Academy of Sciences of the United States of America
影响因子：
11.1
作者：
Lu, Fei;Zhong, Ming;Tang, Sui;Maggioni, Mauro
通讯作者：
Maggioni, Mauro

Conditional regression for single-index models

单指标模型的条件回归