We also introduce a new Gated Recursive Cell to compose lower levelrepresentations into higher level representation. We demonstrate that our modelachieves strong performance on the logical inference task (Bowman et al., 2015)andtheListOps(NangiaandBowman,2018)task. Wecanalsointerpretthemodelto retrieve the induced tree structure, and find that these induced structures alignwith the ground truth.

Using these smoothed rank andsort operators, we propose differentiable proxies for the classification 0/1 loss as well as for the quantileregressionloss.

In an attempt to reduce their description size, we study low rank approximation of such matrices.

For details on M1-M5, please refer to Appendix B.3.

To overcome this limitation, we propose a novel blockwise parallel decoding scheme in which we makepredictions for multiple time steps inparallel then back offtothe longest prefix validated byascoring model.

Causal effect identification considers whether an interventional probability distribution can be uniquely determined from a passively observed distribution in a given causal structure.

However, only the smooth setting was considered. To close this gap, we introduce and analyze O-ZD, the first structured finite-difference algorithm for non-smooth black-box optimization.

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