Thurstonian Boltzmann machines: Learning from multiple inequalities
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 co...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
International Machine Learning Society (IMLS)
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/7956 |