Graph search is one of the most successful algorithmic trends in near neighbor search. Severalofthemostpopular andempirically successful algorithms are,at their core, a greedy walk along a pruned near neighbor graph.

Weprovideatightregretanalysis forExpTS, whichsimultaneously yields both the finite-timeregret bound as well as the asymptotic regret bound.

Neural Information Processing Systems http://nips.cc/

NPI contributes structural biases in the form of modularity, hierarchy and recursion, which are helpful to reduce sample complexity, improve generalization and increase interpretability. AlphaZero contributes powerful neural network guided search algorithms, which we augment with recursion. AlphaNPI only assumes a hierarchical program specification with sparse rewards: 1 when the program execution satisfies the specification, and 0otherwise. This specification enables us to overcome the need for strong supervision in the form of execution traces andconsequently trainNPImodels effectivelywithreinforcement learning.

Achieving a balance between image quality (precision) and diversity (recall) is a significant challenge in the domain of generative models.

Bilinear feature transformation has shown the state-of-the-art performance in learning fine-grained image representations.

The proposed group bilinear requires the intra-group channels to be highly5 correlated (refer tothedefinitioninQ3.1),andtheproposed semantic grouping canbetter satisfy suchrequirements6 than MA-CNN [9]. Specifically,[9] adopts the idea of k-means, which optimizes each channel to its cluster center.7 Note that the notations aboveare the same with Eqn.16 (3),andthepairwisecorrelationis dij = Thanks foryour comments.Aisanapproximate indexmapping20 matrix, whose rows are constrained to be (approximate) one-hot vectors via asoftmax with small "temperature".21 Q2.2Inconsistentnotations. Thanks for your comments, and we will correct the notation "stage 3,4" into "Stage28 IV,V"respectively. Designing suitable grouping methods plays a42 keyrole.

Neural Information Processing Systems http://nips.cc/