[计算机科学] 基于高斯过程的线性潜力模型 [推广有奖]

摘要翻译：
纯数据驱动的机器学习方法在相对于模型的复杂性而言数据稀少或模型被迫外推时存在困难。另一方面，纯机械方法需要识别和指定手头问题中的所有交互（这可能是不可行的），并且仍然留下如何参数化系统的问题。在本文中，我们提出了一种混合方法，利用高斯过程和微分方程，将数据驱动建模与系统的物理模型相结合。我们展示了如何通过对底层系统的合理、简单、机械的假设来开发不同的、物理启发的内核函数。从运动捕捉、计算生物学和地质统计学的三个案例说明了我们方法的通用性。
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英文标题：
《Linear Latent Force Models using Gaussian Processes》
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作者：
Mauricio A. \'Alvarez and David Luengo and Neil D. Lawrence
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最新提交年份：
2020
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分类信息：

一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Purely data driven approaches for machine learning present difficulties when data is scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how to parameterize the system. In this paper, we present a hybrid approach using Gaussian processes and differential equations to combine data driven modelling with a physical model of the system. We show how different, physically-inspired, kernel functions can be developed through sensible, simple, mechanistic assumptions about the underlying system. The versatility of our approach is illustrated with three case studies from motion capture, computational biology and geostatistics.
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PDF链接：
https://arxiv.org/pdf/1107.2699