Thurstonian Boltzmann Machines：从多重不等式中学习
Thurstonian Boltzmann Machines: Learning from Multiple Inequalities
摘要：
我们介绍瑟斯顿玻尔兹曼机（TBM），一个统一的架构，可以自然地纳入广泛的数据输入在同一时间。我们的动机在于Thurstonian的观点，许多离散数据类型可以被认为是从潜在的连续变量的子集生成的，并在观察到，每个实现的离散类型对这些变量施加一定的不平等。因此，TBM中的学习和推理归结为对一组不等式的理解。我们提出的TBM自然支持以下类型：高斯，区间，删失，二进制，分类，多类别，有序，（在）完整的排名与不带领带。我们证明了所提出的模型的多功能性和能力上的三个应用程序的性质非常不同，即手写数字识别，协同过滤和复杂的社会调查分析。
英文摘要：
We introduce Thurstonian Boltzmann Machines (TBM), a unified architecture that can naturally incorporate a wide range of data inputs at the same time. Our motivation rests in the Thurstonian view that many discrete data types can be considered as being generated from a subset of underlying latent continuous variables, and in the observation that each realisation of a discrete type imposes certain inequalities on those variables. Thus learning and inference in TBM reduce to making sense of a set of inequalities. Our proposed TBM naturally supports the following types: Gaussian, intervals, censored, binary, categorical, muticategorical, ordinal, (in)-complete rank with and without ties. We demonstrate the versatility and capacity of the proposed model on three applications of very different natures; namely handwritten digit recognition, collaborative filtering and complex social survey analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
