NSF Convergence Accelerator Track D: Towards Intelligent Sharing and Search for AI Models and Datasets

NSF 融合加速器轨道 D:迈向人工智能模型和数据集的智能共享和搜索

基本信息

  • 批准号:
    2040727
  • 负责人:
  • 金额:
    $ 94.72万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-09-15 至 2022-12-31
  • 项目状态:
    已结题

项目摘要

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. A major goal of AI-driven applications is to discover the underlying patterns in domain-specific datasets, which typically requires tremendous field experience and interdisciplinary knowledge to design or even select suitable AI models. This project will develop a hub and portal for AI data sets and models. It will offer data and model matching recommendations, the use of domain knowledge to improve search strategies for data sets and models, and support for privacy. The hub and portal will engage a broad range of users (in STEM and non-STEM fields) creating AI-driven innovations in various domains that we can only imagine today. Successful execution will provide new tangible artifacts consisting of model and data schemas, software, systems, and services that would make the AI models and datasets easily discoverable, accessible, interoperable, and reproducible.Four novel techniques will be used to realize the envisioned system: (1) A fine-grained privacy control technique with adaptive descriptive statistics, achieving a balance between the privacy needs of data owners and application-driven usability. All other components will have access to only the privacy-controlled data; (2) An automated metadata generation method that exploits various kinds of information about AI models and datasets (e.g., data values, model parameters, auxiliary descriptions) to incorporate domain logic into semantics. This metadata, together with the models and datasets, will be organized as a text-rich network; (3) A representation learning method that transforms information in the text-rich network into a latent space, where datasets/models with similar semantics would be close to each other. This learning over multimodal data will enable comprehensive understandings about models and datasets; (4) A learning-to-match model with constraints will be built to bridge datasets and models. The constraints are mainly induced from schema alignment between models and datasets, which can also filter out obvious non-compatible model and dataset choices, significantly expediting the search and matching process.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.
NSF融合加速器支持以使用为灵感,以团队为基础,多学科的努力,解决国家重要性的挑战,并将在不久的将来产生对社会有价值的可交付成果。人工智能驱动的应用程序的一个主要目标是发现特定领域数据集中的潜在模式,这通常需要大量的领域经验和跨学科知识来设计甚至选择合适的人工智能模型。该项目将为人工智能数据集和模型开发一个中心和门户。它将提供数据和模型匹配建议,使用领域知识来改进数据集和模型的搜索策略,并支持隐私。该中心和门户将吸引广泛的用户(在STEM和非STEM领域),在我们今天只能想象的各个领域创造人工智能驱动的创新。成功的执行将提供由模型和数据模式、软件、系统和服务组成的新的有形构件,这些构件将使人工智能模型和数据集易于发现、访问、互操作和再现。该系统将采用四种新技术:(1)采用自适应描述性统计的细粒度隐私控制技术,在数据所有者的隐私需求和应用驱动的可用性之间实现平衡。所有其他组件只能访问受隐私控制的数据;(2)一种自动元数据生成方法,利用人工智能模型和数据集的各种信息(如数据值、模型参数、辅助描述)将领域逻辑纳入语义。这些元数据,连同模型和数据集,将被组织成一个文本丰富的网络;(3)一种将富文本网络中的信息转化为潜在空间的表示学习方法,在潜在空间中,语义相似的数据集/模型彼此接近。这种对多模态数据的学习将使对模型和数据集的全面理解成为可能;(4)建立带有约束的学习匹配模型,将数据集和模型连接起来。这些约束主要来自模型和数据集之间的模式对齐,它还可以过滤掉明显不兼容的模型和数据集选择,显著加快搜索和匹配过程。该奖项反映了美国国家科学基金会的法定使命,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。

项目成果

期刊论文数量(28)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
“Misc”-Aware Weakly Supervised Aspect Classification
–Misc – 感知弱监督方面分类
TARNet: Task-Aware Reconstruction for Time-Series Transformer
Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification
  • DOI:
    10.48550/arxiv.2301.11459
  • 发表时间:
    2023-01
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Zi Lin;J. Liu;Jingbo Shang
  • 通讯作者:
    Zi Lin;J. Liu;Jingbo Shang
Sensei: Self-Supervised Sensor Name Segmentation
Sensei:自监督传感器名称分割
Sharing personal ECG time-series data privately
私下共享个人心电图时间序列数据
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Jingbo Shang其他文献

Towards Zero-shot Relation Extraction in Web Mining: A Multimodal Approach with Relative XML Path
迈向 Web 挖掘中的零样本关系提取:具有相对 XML 路径的多模式方法
  • DOI:
    10.48550/arxiv.2305.13805
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Zilong Wang;Jingbo Shang
  • 通讯作者:
    Jingbo Shang
Less than One-shot: Named Entity Recognition via Extremely Weak Supervision
不到一次:通过极弱监督进行命名实体识别
Involvement of poly(ADP-ribose) polymerase-1 in development of spinal cord injury in Chinese individuals: a Chinese clinical study
聚(ADP-核糖)聚合酶-1 参与中国人脊髓损伤的发生:一项中国临床研究
  • DOI:
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Qingyang Meng;Guang;Renbo Li;Jing;Wei Zhou;Hong;Bo Chen;Li Jiang;Jingbo Shang
  • 通讯作者:
    Jingbo Shang
AI-native Memory: A Pathway from LLMs Towards AGI
AI 原生内存:从法学硕士迈向 AGI 的途径
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Jingbo Shang;Zai Zheng;Xiang Ying;Felix Tao;Mindverse Team
  • 通讯作者:
    Mindverse Team
CubeNet: Multi-Facet Hierarchical Heterogeneous Network Construction, Analysis, and Mining
CubeNet:多方面分层异构网络构建、分析和挖掘
  • DOI:
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Carl Yang;Dai Teng;Siyang Liu;Sayantan Basu;Jieyu Zhang;Jiaming Shen;Chao Zhang;Jingbo Shang;Lance M. Kaplan;Timothy Harratty;Jiawei Han
  • 通讯作者:
    Jiawei Han

Jingbo Shang的其他文献

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{{ truncateString('Jingbo Shang', 18)}}的其他基金

CAREER: Knowledge Extraction and Discovery from Massive Text Corpora via Extremely Weak Supervision
职业:通过极弱监督从海量文本语料库中提取和发现知识
  • 批准号:
    2239440
  • 财政年份:
    2023
  • 资助金额:
    $ 94.72万
  • 项目类别:
    Continuing Grant

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