75 citations · 473 across the 72 of their papers we have counts for
14 papers · 1 filter
Characterizing the Evasion Attackability of Multi-label Classifiers
Zhuo Yang, Yufei Han, Xiangliang Zhang
Evasion attack in multi-label learning systems is an interesting, widely witnessed, yet rarely explored research topic. Characterizing the crucial factors determining the attackabi…
Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation
Xin Xia, Hongzhi Yin, Junliang Yu +3
Session-based recommendation (SBR) focuses on next-item prediction at a certain time point. As user profiles are generally not available in this scenario, capturing the user intent…
ASAD: A Twitter-based Benchmark Arabic Sentiment Analysis Dataset
Basma Alharbi, Hind Alamro, Manal Alshehri +4
This paper provides a detailed description of a new Twitter-based benchmark dataset for Arabic Sentiment Analysis (ASAD), which is launched in a competition3, sponsored by KAUST fo…
Deterministic and probabilistic deep learning models for inverse design of broadband acoustic cloak
Waqas W. Ahmed, Mohamed Farhat, Xiangliang Zhang +1
Concealing an object from incoming waves (light and/or sound) remained science fiction for a long time due to the absence of wave-shielding materials in nature. Yet, the invention…
Multi-typed Objects Multi-view Multi-instance Multi-label Learning
Yuanlin Yang, Guoxian Yu, Jun Wang +2
Multi-typed objects Multi-view Multi-instance Multi-label Learning (M4L) deals with interconnected multi-typed objects (or bags) that are made of diverse instances, represented wit…
Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation
Uchenna Akujuobi, Jun Chen, Mohamed Elhoseiny +2
Understanding the relationships between biomedical terms like viruses, drugs, and symptoms is essential in the fight against diseases. Many attempts have been made to introduce the…
Deep Incomplete Multi-View Multiple Clusterings
Shaowei Wei, Jun Wang, Guoxian Yu +2
Multi-view clustering aims at exploiting information from multiple heterogeneous views to promote clustering. Most previous works search for only one optimal clustering based on th…
SAIL: Self-Augmented Graph Contrastive Learning
Lu Yu, Shichao Pei, Lizhong Ding +4
This paper studies learning node representations with graph neural networks (GNNs) for unsupervised scenario. Specifically, we derive a theoretical analysis and provide an empirica…
PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization
Zhize Li, Hongyan Bao, Xiangliang Zhang +1
In this paper, we propose a novel stochastic gradient estimator -- ProbAbilistic Gradient Estimator (PAGE) -- for nonconvex optimization. PAGE is easy to implement as it is designe…
Graph Factorization Machines for Cross-Domain Recommendation
Dongbo Xi, Fuzhen Zhuang, Yongchun Zhu +3
Recently, graph neural networks (GNNs) have been successfully applied to recommender systems. In recommender systems, the user's feedback behavior on an item is usually the result…
SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic
Qiang Yang, Hind Alamro, Somayah Albaradei +11
Since the first alert launched by the World Health Organization (5 January, 2020), COVID-19 has been spreading out to over 180 countries and territories. As of June 18, 2020, in to…
Addressing Class-Imbalance Problem in Personalized Ranking
Lu Yu, Shichao Pei, Chuxu Zhang +4
Pairwise ranking models have been widely used to address recommendation problems. The basic idea is to learn the rank of users' preferred items through separating items into \emph{…
Efficient long-distance relation extraction with DG-SpanBERT
Jun Chen, Robert Hoehndorf, Mohamed Elhoseiny +1
In natural language processing, relation extraction seeks to rationally understand unstructured text. Here, we propose a novel SpanBERT-based graph convolutional network (DG-SpanBE…
Attention-Aware Answers of the Crowd
Jingzheng Tu, Guoxian Yu, Jun Wang +2
Crowdsourcing is a relatively economic and efficient solution to collect annotations from the crowd through online platforms. Answers collected from workers with different expertis…