32 citations · 113 across the 16 of their papers we have counts for
8 papers · 1 filter
Gradient Leakage Attack Resilient Deep Learning
Wenqi Wei, Ling Liu
Gradient leakage attacks are considered one of the wickedest privacy threats in deep learning as attackers covertly spy gradient updates during iterative training without compromis…
Learning Text-Image Joint Embedding for Efficient Cross-Modal Retrieval with Deep Feature Engineering
Zhongwei Xie, Ling Liu, Yanzhao Wu +2
This paper introduces a two-phase deep feature engineering framework for efficient learning of semantics enhanced joint embedding, which clearly separates the deep feature engineer…
Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding
Jingya Zhou, Ling Liu, Wenqi Wei +1
Network representation learning (NRL) advances the conventional graph mining of social networks, knowledge graphs, and complex biomedical and physics information networks. Over doz…
Learning Joint Embedding with Modality Alignments for Cross-Modal Retrieval of Recipes and Food Images
Zhongwei Xie, Ling Liu, Lin Li +1
This paper presents a three-tier modality alignment approach to learning text-image joint embedding, coined as JEMA, for cross-modal retrieval of cooking recipes and food images. T…
Efficient Deep Feature Calibration for Cross-Modal Joint Embedding Learning
Zhongwei Xie, Ling Liu, Lin Li +1
This paper introduces a two-phase deep feature calibration framework for efficient learning of semantics enhanced text-image cross-modal joint embedding, which clearly separates th…
Learning TFIDF Enhanced Joint Embedding for Recipe-Image Cross-Modal Retrieval Service
Zhongwei Xie, Ling Liu, Yanzhao Wu +2
It is widely acknowledged that learning joint embeddings of recipes with images is challenging due to the diverse composition and deformation of ingredients in cooking procedures.…