4 papers
Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement
Zhiqiang Shen, Zhankui He, Wanyun Cui +4
Generic Image recognition is a fundamental and fairly important visual problem in computer vision. One of the major challenges of this task lies in the fact that single image usual…
MEAL: Multi-Model Ensemble via Adversarial Learning
Zhiqiang Shen, Zhankui He, Xiangyang Xue
Often the best performing deep neural models are ensembles of multiple base-level networks. Unfortunately, the space required to store these many networks, and the time required to…
NAIS: Neural Attentive Item Similarity Model for Recommendation
Xiangnan He, Zhankui He, Jingkuan Song +3
Item-to-item collaborative filtering (aka. item-based CF) has been long used for building recommender systems in industrial settings, owing to its interpretability and efficiency i…
Adversarial Personalized Ranking for Recommendation
Xiangnan He, Zhankui He, Xiaoyu Du +1
Item recommendation is a personalized ranking task. To this end, many recommender systems optimize models with pairwise ranking objectives, such as the Bayesian Personalized Rankin…