Deep Lattice Networks and Partial Monotonic Functions

NeurIPS 2017 Seungil YouDavid DingKevin CaniniJan PfeiferMaya Gupta

We propose learning deep models that are monotonic with respect to a user-specified set of inputs by alternating layers of linear embeddings, ensembles of lattices, and calibrators (piecewise linear functions), with appropriate constraints for monotonicity, and jointly training the resulting network. We implement the layers and projections with new computational graph nodes in TensorFlow and use the ADAM optimizer and batched stochastic gradients... (read more)

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