Generative Modelling and Representation Learning
生成建模和表示学习
基本信息
- 批准号:2420772
- 负责人:
- 金额:--
- 依托单位:
- 依托单位国家:英国
- 项目类别:Studentship
- 财政年份:2020
- 资助国家:英国
- 起止时间:2020 至 无数据
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Raw data is abundant in the modern world. Creating models that can make sense of this large flow of information would be very helpful for many tasks. Unfortunately, most data is not neatly packaged in a format that traditional machine learning methods can use. It is desirable to have methods that can extract useful information from data in any format. A learning paradigm that meets this criteria is generative modelling. Generative models look at example data from a certain source and learn to synthesize new fake data that looks like the original real data. Synthesizing fake examples may not be useful for many applications in and of itself. However, inherent in the ability to create realistic examples is a deep understand of the structure and form of the data. Therefore, within these generative models there must exist 'representations' of the data that summarize pertinent aspects of the data such as its structure and form. These representations are useful for both humans and further models. The generative models can be coaxed into producing representations that are interpretable to humans providing us with automatic and extensive summaries of large amounts of data that go beyond simple metrics such as the mean and variance. Parallel to this, further models can be trained directly on the representations of the data instead of on the raw data itself. Since the representations contain condensed information about the data learnt by the original generative model, it is usually the case that models trained on representations require much less overall data to achieve the same level of performance as a model trained on the raw data directly.This project aims to improve upon existing generative modelling techniques as well as formulate new ways to extract representations from learnt generative models. New methods for generative modelling are regularly proposed in the research community but our understanding of exactly what is being learnt from the data and how to extract this information often lags behind. The project will deal with very novel methods for generative modelling and aims to bring our understanding of their inner workings up to speed. This will be achieved through a combination of empirical investigation into state of the art models as well as theoretical work to characterise their behaviours.This project falls within the EPSRC Artifical Intelligence and Robotics research area.Collaboration is currently planned within the Oxford University Department of Statistics internally.
现代世界的原始数据非常丰富。创建能够理解这种大量信息流的模型对于许多任务都非常有帮助。不幸的是,大多数数据并没有以传统机器学习方法可以使用的格式整齐地打包。希望有能够从任何格式的数据中提取有用信息的方法。满足这一标准的学习范式是生成建模。生成式模型查看来自特定来源的示例数据,并学习合成看起来像原始真实的数据的新假数据。合成假例子本身可能对许多应用程序没有用处。然而,创建真实示例的能力所固有的是对数据结构和形式的深刻理解。因此,在这些生成模型中,必须存在数据的“表示”,这些表示总结了数据的相关方面,例如其结构和形式。这些表示对于人类和其他模型都很有用。生成模型可以被诱导产生人类可解释的表示,为我们提供大量数据的自动和广泛的摘要,这些数据超出了简单的指标,如均值和方差。与此并行,可以直接在数据的表示上而不是在原始数据本身上训练其他模型。由于表示包含原始生成模型学习的数据的浓缩信息,通常情况下,使用表示训练的模型需要更少的整体数据才能达到与直接使用原始数据训练的模型相同的性能水平。该项目旨在改进现有的生成建模技术,并制定新的方法来从学习的生成模型中提取表示。研究界经常提出生成建模的新方法,但我们对从数据中确切了解到什么以及如何提取这些信息的理解往往滞后。该项目将处理非常新颖的生成建模方法,旨在使我们对其内部工作原理的理解达到最高速度。这将通过对最先进的模型进行实证调查以及理论工作来验证其行为的结合来实现。该项目福尔斯EPSRC机器人智能和机器人研究领域。目前计划在牛津大学统计系内部进行合作。
项目成果
期刊论文数量(0)
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其他文献
吉治仁志 他: "トランスジェニックマウスによるTIMP-1の線維化促進機序"最新医学. 55. 1781-1787 (2000)
Hitoshi Yoshiji 等:“转基因小鼠中 TIMP-1 的促纤维化机制”现代医学 55. 1781-1787 (2000)。
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LiDAR Implementations for Autonomous Vehicle Applications
- DOI:
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2021 - 期刊:
- 影响因子:0
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吉治仁志 他: "イラスト医学&サイエンスシリーズ血管の分子医学"羊土社(渋谷正史編). 125 (2000)
Hitoshi Yoshiji 等人:“血管医学与科学系列分子医学图解”Yodosha(涉谷正志编辑)125(2000)。
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Effect of manidipine hydrochloride,a calcium antagonist,on isoproterenol-induced left ventricular hypertrophy: "Yoshiyama,M.,Takeuchi,K.,Kim,S.,Hanatani,A.,Omura,T.,Toda,I.,Akioka,K.,Teragaki,M.,Iwao,H.and Yoshikawa,J." Jpn Circ J. 62(1). 47-52 (1998)
钙拮抗剂盐酸马尼地平对异丙肾上腺素引起的左心室肥厚的影响:“Yoshiyama,M.,Takeuchi,K.,Kim,S.,Hanatani,A.,Omura,T.,Toda,I.,Akioka,
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