权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

MRI: Acquisition of a GPU cluster to support interdisciplinary research in human learning, machine learning, and data science

MRI：收购 GPU 集群以支持人类学习、机器学习和数据科学的跨学科研究

基本信息

批准号：
1828528
负责人：
Patrick Shafto
金额：
$ 10万
依托单位：
Rutgers University Newark
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-01 至 2021-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1828528&HistoricalAwards=false
关键词：
MRI Acquisition GPU cluster support

项目摘要

This Major Instrumentation Grant award supports the Acquisition of a GPU cluster to support interdisciplinary research in human learning, machine learning, and data science at Rutgers University--Newark, a Minority Serving Institution (MSI). It permits purchase of 3 Nvidia dual V100 GPUs to enable theoretical advances and practical applications in interdisciplinary understanding of learning. Rutgers-Newark is undertaking a multiyear effort to build strength in interdisciplinary computer science to support research training, and to address issues of diversity and representation within computer science and data science. These resources would: (1) enable the application of computationally-intensive methods in order to develop new theories and tools to understand human and machine learning; (2) support existing cross-disciplinary training efforts, such as graduate-level courses centered around deep learning and Deep Gaussian Processes; (3) enhance existing funded research by allowing the deployment of advanced data-analytic methods. The GPU cluster will provide a common computational resource for researchers from the Computer Science, Psychology, and Neuroscience departments through which they may collaborate to advance the state-of-the-art in each field. This purchase will complement the existing high-performance computing infrastructure already on campus as well as a recent NSF-supported purchase of a 1.2 petabyte storage system for cataloging the dynamics of human visual experience. Also, it will supplement an NSF-sponsored Mobile Maker Center for community-based data collection and fMRI research. Humans remain the most powerful and impressive available models of learning, although the roots of these abilities are not fully understood. Although machine learning methods have become exceptionally powerful in recent years, they remain opaque in ways that human learning is not and still require vastly more data, energy and compute power than human learners. Both human and machine learning would benefit from the ability to more tightly connect and study the strengths of each. Gaussian processes provide one such unifying framework. They are an object of interest in machine learning, where they have dual interpretations as regression models and as neural networks, as well as in human learning where they have been proposed as models of cognition and perception. These multiple interpretations of Gaussian processes are key to their interest for bridging human and machine learning. From a theoretical perspective, Gaussian processes are equivalent to (a specific type of) neural network, but much more amenable to mathematical analysis, and can be stacked to obtain Deep Gaussian processes. This Deep learning framework may allow more systematic mathematical analysis than other Deep learning approaches---for example the ability to derive explanations for their inferences. The primary research goal of this project is to use the GPU cluster and the investigators' interdisciplinary expertise to draw deep connections between machine learning and human learning perspectives to advance the state of the art in both, while also improving data analytic capabilities.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.

该重大仪器补助金支持收购 GPU 集群，以支持少数族裔服务机构 (MSI) 罗格斯大学纽瓦克分校在人类学习、机器学习和数据科学方面的跨学科研究。它允许购买 3 个 Nvidia 双 V100 GPU，以实现跨学科学习理解的理论进步和实际应用。罗格斯-纽瓦克大学正在开展多年的努力，以增强跨学科计算机科学的实力，以支持研究培训，并解决计算机科学和数据科学中的多样性和代表性问题。这些资源将：（1）支持计算密集型方法的应用，以开发新的理论和工具来理解人类和机器学习；（2）支持现有的跨学科培训工作，例如以深度学习和深度高斯过程为中心的研究生课程； (3) 通过允许部署先进的数据分析方法来加强现有的资助研究。 GPU集群将为计算机科学、心理学和神经科学系的研究人员提供通用的计算资源，他们可以通过这些资源合作推进每个领域的最先进技术。此次购买将补充校园内现有的高性能计算基础设施，以及最近 NSF 支持购买的 1.2 PB 存储系统，用于对人类视觉体验的动态进行编目。此外，它还将补充 NSF 资助的移动创客中心，用于基于社区的数据收集和功能磁共振成像研究。人类仍然是最强大、最令人印象深刻的可用学习模型，尽管这些能力的根源尚未完全了解。尽管机器学习方法近年来变得异常强大，但它们仍然是人类学习所不具备的不透明性，并且仍然需要比人类学习者更多的数据、能量和计算能力。人类和机器学习都将受益于更紧密地联系和研究各自优势的能力。高斯过程提供了一个这样的统一框架。它们是机器学习中令人感兴趣的对象，在机器学习中它们具有回归模型和神经网络的双重解释，在人类学习中它们被提议作为认知和感知模型。对高斯过程的这些多重解释是他们对连接人类和机器学习感兴趣的关键。从理论角度来看，高斯过程相当于（特定类型的）神经网络，但更适合数学分析，并且可以叠加以获得深度高斯过程。与其他深度学习方法相比，这种深度学习框架可能允许更系统的数学分析——例如为其推论得出解释的能力。该项目的主要研究目标是利用 GPU 集群和研究人员的跨学科专业知识，在机器学习和人类学习观点之间建立深刻的联系，以推进两者的最先进水平，同时提高数据分析能力。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。