Thurstonian Boltzmann Machines: Learning from Multiple Inequalities

1 Aug 2014 Truyen Tran Dinh Phung Svetha Venkatesh

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... (read more)

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