Technology
ArchimedesMeetsPrivacy: OnPrivatelyEstimatingQuantilesinHighDimensions UnderMinimalAssumptions
Computing statistics of large, complex high-dimensional datasets under privacy constraints is a fundamental challenge in modern data science.
Provably Efficient Q-Learning with Low Switching Cost
Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang
Neural Information Processing Systems http://nips.cc/
Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries
Fuwen Tan, Paola Cascante-Bonilla, Xiaoxiao Guo, Hui Wu, Song Feng, Vicente Ordonez
Neural Information Processing Systems http://nips.cc/
471c75ee6643a10934502bdafee198fb-AuthorFeedback.pdf
Patternsofrealuserqueries10 Compared with the simulated caption data, the real user queries we collected show distinct patterns, e.g. Potential applications include22 retrieving very specific images of complex scenes the users encountered before, or exploring inspiring images for23 creativecontentgeneration(e.g. Weconjecture that unidirectional and bidirectional encoders provide comparable contextual signals when28 encoding theper-turn query asasingle feature vector fordownstream modules.