2014/08/01 by Truyen Tran, Tran, Truyen, Dinh Phung +3 · 1 citation
Computer Science · #Generative Adversarial Networks and Image Synthesis #Music and Audio Processing #Domain Adaptation and Few-Shot Learning
paper · pdf · doi:10.48550/arxiv.1408.0055
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.