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.

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

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.

In real-world sequential prediction scenarios, the features (or attributes) of examples are typically high-dimensional and construction of the all features for each example may be expensive or impossible.

Our nonconvex optimization formulation solves for a filter h on the unit sphere that produces sparse outputyi ~h.

Furthermore, it exceeds GroundingDINO-L by 11.0 AP

Inthispaper,weproposeareciprocative learning algorithm to exploit visual attention for training deep classifiers.