2.4k citations · 7.9k across the 182 of their papers we have counts for
25 papers · 1 filter
Node Similarity Preserving Graph Convolutional Networks
Wei Jin, Tyler Derr, Yiqi Wang +3
Graph Neural Networks (GNNs) have achieved tremendous success in various real-world applications due to their strong ability in graph representation learning. GNNs explore the grap…
Personalized Multimodal Feedback Generation in Education
Haochen Liu, Zitao Liu, Zhongqin Wu +1
The automatic evaluation for school assignments is an important application of AI in the education field. In this work, we focus on the task of personalized multimodal feedback gen…
Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning
Haochen Liu, Wentao Wang, Yiqi Wang +3
Dialogue systems play an increasingly important role in various aspects of our daily life. It is evident from recent research that dialogue systems trained on human conversation da…
To be Robust or to be Fair: Towards Fairness in Adversarial Training
Han Xu, Xiaorui Liu, Yaxin Li +2
Adversarial training algorithms have been proved to be reliable to improve machine learning models' robustness against adversarial examples. However, we find that adversarial train…
A Unified View on Graph Neural Networks as Graph Signal Denoising
Yao Ma, Xiaorui Liu, Tong Zhao +3
Graph Neural Networks (GNNs) have risen to prominence in learning representations for graph structured data. A single GNN layer typically consists of a feature transformation and a…
Representation Learning from Limited Educational Data with Crowdsourced Labels
Wentao Wang, Guowei Xu, Wenbiao Ding +4
Representation learning has been proven to play an important role in the unprecedented success of machine learning models in numerous tasks, such as machine translation, face recog…
Road to the White House: Analyzing the Relations Between Mainstream and Social Media During the U.S. Presidential Primaries
Aaron Brookhouse, Tyler Derr, Hamid Karimi +2
Information is crucial to the function of a democratic society where well-informed citizens can make rational political decisions. While in the past political entities were primari…
Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection
Zhiwei Wang, Zhengzhang Chen, Jingchao Ni +3
Discrete event sequences are ubiquitous, such as an ordered event series of process interactions in Information and Communication Technology systems. Recent years have witnessed in…
Linear Convergent Decentralized Optimization with Compression
Xiaorui Liu, Yao Li, Rongrong Wang +2
Communication compression has become a key strategy to speed up distributed optimization. However, existing decentralized algorithms with compression mainly focus on compressing DG…
Graph Structure Learning for Robust Graph Neural Networks
Wei Jin, Yao Ma, Xiaorui Liu +3
Graph Neural Networks (GNNs) are powerful tools in representation learning for graphs. However, recent studies show that GNNs are vulnerable to carefully-crafted perturbations, cal…
Self-supervised Learning on Graphs: Deep Insights and New Direction
Wei Jin, Tyler Derr, Haochen Liu +4
The success of deep learning notoriously requires larger amounts of costly annotated data. This has led to the development of self-supervised learning (SSL) that aims to alleviate…
XGNN: Towards Model-Level Explanations of Graph Neural Networks
Hao Yuan, Jiliang Tang, Xia Hu +1
Graphs neural networks (GNNs) learn node features by aggregating and combining neighbor information, which have achieved promising performance on many graph tasks. However, GNNs ar…
Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks
Xianfeng Tang, Huaxiu Yao, Yiwei Sun +5
Graph Convolutional Networks (GCNs) show promising results for semi-supervised learning tasks on graphs, thus become favorable comparing with other approaches. Despite the remarkab…
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…
Chat as Expected: Learning to Manipulate Black-box Neural Dialogue Models
Haochen Liu, Zhiwei Wang, Tyler Derr +1
Recently, neural network based dialogue systems have become ubiquitous in our increasingly digitalized society. However, due to their inherent opaqueness, some recently raised conc…
Neural Multi-Task Learning for Teacher Question Detection in Online Classrooms
Gale Yan Huang, Jiahao Chen, Haochen Liu +6
Asking questions is one of the most crucial pedagogical techniques used by teachers in class. It not only offers open-ended discussions between teachers and students to exchange id…
Siamese Neural Networks for Class Activity Detection
Hang Li, Zhiwei Wang, Jiliang Tang +2
Classroom activity detection (CAD) aims at accurately recognizing speaker roles (either teacher or student) in classrooms. A CAD solution helps teachers get instant feedback on the…
DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses
Yaxin Li, Wei Jin, Han Xu +1
DeepRobust is a PyTorch adversarial learning library which aims to build a comprehensive and easy-to-use platform to foster this research field. It currently contains more than 10…
Customized Graph Neural Networks
Yiqi Wang, Yao Ma, Wei Jin +3
Recently, Graph Neural Networks (GNNs) have greatly advanced the task of graph classification. Typically, we first build a unified GNN model with graphs in a given training set and…
Attacking Black-box Recommendations via Copying Cross-domain User Profiles
Wenqi Fan, Tyler Derr, Xiangyu Zhao +5
Recently, recommender systems that aim to suggest personalized lists of items for users to interact with online have drawn a lot of attention. In fact, many of these state-of-the-a…
Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies
Wei Jin, Yaxin Li, Han Xu +4
Deep neural networks (DNNs) have achieved significant performance in various tasks. However, recent studies have shown that DNNs can be easily fooled by small perturbation on the i…
Jointly Learning to Recommend and Advertise
Xiangyu Zhao, Xudong Zheng, Xiwang Yang +2
Online recommendation and advertising are two major income channels for online recommendation platforms (e.g. e-commerce and news feed site). However, most platforms optimize recom…
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations
Xiangyu Zhao, Chong Wang, Ming Chen +3
Deep learning based recommender systems (DLRSs) often have embedding layers, which are utilized to lessen the dimensionality of categorical variables (e.g. user/item identifiers) a…
Exploring Spatio-Temporal and Cross-Type Correlations for Crime Prediction
Xiangyu Zhao, Jiliang Tang
Crime prediction plays an impactful role in enhancing public security and sustainable development of urban. With recent advances in data collection and integration technologies, a…