论文标题
镜头机：代表潜在变量模型中的透视图
Lensing Machines: Representing Perspective in Latent Variable Models
论文作者
论文摘要
许多数据集代表着不同方式来查看导致不同概括的相同数据的组合。例如，具有不同人产生的示例的语料库可能是许多观点的混合物，并且可以从其他人看待不同的观点。不可能通过代表每个观点的示例的清洁分离来表示观点，并为每个观点训练单独的模型。我们介绍了镜头，这是一种混合的主动技术，可在机器学到的人类专家的镜头和人类专家的观点之间提取镜头或映射，并生成具有相同数据集多种观点的镜头模型。我们将镜头应用于两个类别的潜在变量模型：混合成员模型，两个心理健康应用背景下的矩阵分解模型，我们将临床心理学家的观点捕获和浸入这些模型中。我们的工作显示了机器学习从业者的好处，正式将知识渊博的领域专家的观点纳入了他们的模型，而不是孤立地估算出释放的模型。
Many datasets represent a combination of different ways of looking at the same data that lead to different generalizations. For example, a corpus with examples generated by different people may be mixtures of many perspectives and can be viewed with different perspectives by others. It isnt always possible to represent the viewpoints by a clean separation, in advance, of examples representing each viewpoint and train a separate model for each viewpoint. We introduce lensing, a mixed initiative technique to extract lenses or mappings between machine learned representations and perspectives of human experts, and to generate lensed models that afford multiple perspectives of the same dataset. We apply lensing for two classes of latent variable models: a mixed membership model, a matrix factorization model in the context of two mental health applications, and we capture and imbue the perspectives of clinical psychologists into these models. Our work shows the benefits of the machine learning practitioner formally incorporating the perspective of a knowledgeable domain expert into their models rather than estimating unlensed models themselves in isolation.
