ENGAGE : Interactive Machine Learning Accelerating Progress in Science, An Emerging Theme of ICT Research

ENGAGE：交互式机器学习加速科学进步，ICT 研究的新兴主题

• 批准号: EP/K015664/2
• 负责人: Mark Girolami
• 金额: $ 66.12万
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FK015664%2F2
• 关键词: ENGAGE; Interactive; Machine; Learning; Accelerating

Our vision is to establish and lead a new theme in ICT research based on Interactive Machine Learning (IML). Our expansion of IML will give scientists and non-ICT specialists unprecedented access to cutting-edge Machine Learning algorithms by providing a human-computer interface by which they can directly interact with large scale data and computing resources in an intuitive visual environment. In addition, the outcome of this particular project will have a direct transformative impact on the sciences by making it possible for non-programming individuals (scientists), to create systems that semi-automatically detect objects and events in vast quantities of A) audio and B) visual data. By working together across two parallel, highly interconnected streams of ICT research, we will develop the foundations of statistical methodology, algorithms and systems for IML. As an exemplar, this project partners with world leading scientists grappling with the challenge of analysing enormous quantities of heterogeneous data being generated in Biodiversity Science.

我们的愿景是建立并引领基于交互式机器学习 (IML) 的 ICT 研究新主题。我们对 IML 的扩展将为科学家和非 ICT 专家提供前所未有的接触尖端机器学习算法的机会，通过提供人机界面，他们可以在直观的视觉环境中直接与大规模数据和计算资源进行交互。此外，这个特定项目的成果将使非编程个人（科学家）能够创建半自动检测大量 A）音频和 B）视觉数据中的对象和事件的系统，从而对科学产生直接的变革性影响。通过在两个并行、高度互连的 ICT 研究领域开展合作，我们将为 IML 开发统计方法、算法和系统的基础。作为一个范例，该项目与世界领先的科学家合作，应对分析生物多样性科学中生成的大量异构数据的挑战。

项目成果

CityNet-Deep learning tools for urban ecoacoustic assessment

• DOI: 10.1111/2041-210x.13114
• 发表时间: 2019-02-01
• 期刊: METHODS IN ECOLOGY AND EVOLUTION
• 影响因子: 6.6
• 作者: Fairbrass, Alison J.;Firman, Michael;Jones, Kate E.
• 通讯作者: Jones, Kate E.

Becoming the expert - interactive multi-class machine teaching

• DOI: 10.1109/cvpr.2015.7298877
• 发表时间: 2015-04
• 期刊: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
• 作者: Edward Johns;Oisin Mac Aodha;G. Brostow

Help, It Looks Confusing

求助，看起来很混乱

• DOI: 10.1145/3025171.3025176
• 发表时间: 2017
• 作者: Intharah T

DiverseNet: When One Right Answer is not Enough

• DOI: 10.1109/cvpr.2018.00587
• 发表时间: 2018-03
• 期刊: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
• 作者: Michael Firman;N. Campbell;L. Agapito;G. Brostow

Responsive Action-based Video Synthesis

基于动作的响应式视频合成

• DOI: 10.1145/3025453.3025880
• 发表时间: 2017
• 作者: Ilisescu C