most citedEfficient Neural Query Auto Completion

24 citations · 55 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2020★ 7 cited

Improving Query Efficiency of Black-box Adversarial Attack

Yang Bai, Yuyuan Zeng, Yong Jiang +3

Deep neural networks (DNNs) have demonstrated excellent performance on various tasks, however they are under the risk of adversarial examples that can be easily generated when the…

cs.CL2020★ 3 cited

Deep Search Query Intent Understanding

Xiaowei Liu, Weiwei Guo, Huiji Gao +1

Understanding a user's query intent behind a search is critical for modern search engine success. Accurate query intent prediction allows the search engine to better serve the user…

cs.CL2020★ 24 cited

Efficient Neural Query Auto Completion

Sida Wang, Weiwei Guo, Huiji Gao +1

Query Auto Completion (QAC), as the starting point of information retrieval tasks, is critical to user experience. Generally it has two steps: generating completed query candidates…

cs.IR2020★ 1 cited

DeText: A Deep Text Ranking Framework with BERT

Weiwei Guo, Xiaowei Liu, Sida Wang +8

Ranking is the most important component in a search system. Mostsearch systems deal with large amounts of natural language data,hence an effective ranking system requires a deep un…

cs.IR2020★ 18 cited

Memory-efficient Embedding for Recommendations

Xiangyu Zhao, Haochen Liu, Hui Liu +6

Practical large-scale recommender systems usually contain thousands of feature fields from users, items, contextual information, and their interactions. Most of them empirically al…

cs.CR2020★ 2 cited

Targeted Attack for Deep Hashing based Retrieval

Jiawang Bai, Bin Chen, Yiming Li +4

The deep hashing based retrieval method is widely adopted in large-scale image and video retrieval. However, there is little investigation on its security. In this paper, we propos…