25 citations · 51 across the 7 of their papers we have counts for
7 papers
Integer-Only Neural Network Quantization Scheme Based on Shift-Batch-Normalization
Qingyu Guo, Yuan Wang, Xiaoxin Cui
Neural networks are very popular in many areas, but great computing complexity makes it hard to run neural networks on devices with limited resources. To address this problem, quan…
Contextual User Browsing Bandits for Large-Scale Online Mobile Recommendation
Xu He, Bo An, Yanghua Li +4
Online recommendation services recommend multiple commodities to users. Nowadays, a considerable proportion of users visit e-commerce platforms by mobile devices. Due to the limite…
A Survey on Knowledge Graph-Based Recommender Systems
Qingyu Guo, Fuzhen Zhuang, Chuan Qin +4
To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users preferences. Although…
Manipulating a Learning Defender and Ways to Counteract
Jiarui Gan, Qingyu Guo, Long Tran-Thanh +2
In Stackelberg security games when information about the attacker's payoffs is uncertain, algorithms have been proposed to learn the optimal defender commitment by interacting with…
Imitative Follower Deception in Stackelberg Games
Jiarui Gan, Haifeng Xu, Qingyu Guo +3
Information uncertainty is one of the major challenges facing applications of game theory. In the context of Stackelberg games, various approaches have been proposed to deal with t…
Multi-Scale Quasi-RNN for Next Item Recommendation
Chaoyue He, Yong Liu, Qingyu Guo +1
How to better utilize sequential information has been extensively studied in the setting of recommender systems. To this end, architectural inductive biases such as Markov-Chains,…
On the Inducibility of Stackelberg Equilibrium for Security Games
Qingyu Guo, Jiarui Gan, Fei Fang +3
Strong Stackelberg equilibrium (SSE) is the standard solution concept of Stackelberg security games. As opposed to the weak Stackelberg equilibrium (WSE), the SSE assumes that the…