22 citations · 36 across the 5 of their papers we have counts for
6 papers
Accelerating Personalized PageRank Vector Computation
Zhen Chen, Xingzhi Guo, Baojian Zhou +2
Personalized PageRank Vectors are widely used as fundamental graph-learning tools for detecting anomalous spammers, learning graph embeddings, and training graph neural networks. T…
Provable Fairness for Neural Network Models using Formal Verification
Giorgian Borca-Tasciuc, Xingzhi Guo, Stanley Bak +1
Machine learning models are increasingly deployed for critical decision-making tasks, making it important to verify that they do not contain gender or racial biases picked up from…
Hierarchies over Vector Space: Orienting Word and Graph Embeddings
Xingzhi Guo, Steven Skiena
Word and graph embeddings are widely used in deep learning applications. We present a data structure that captures inherent hierarchical properties from an unordered flat embedding…
Subset Node Anomaly Tracking over Large Dynamic Graphs
Xingzhi Guo, Baojian Zhou, Steven Skiena
Tracking a targeted subset of nodes in an evolving graph is important for many real-world applications. Existing methods typically focus on identifying anomalous edges or finding a…
Improving Device Directedness Classification of Utterances with Semantic Lexical Features
Kellen Gillespie, Ioannis C. Konstantakopoulos, Xingzhi Guo +2
User interactions with personal assistants like Alexa, Google Home and Siri are typically initiated by a wake term or wakeword. Several personal assistants feature "follow-up" mode…
COMET: Convolutional Dimension Interaction for Collaborative Filtering
Zhuoyi Lin, Lei Feng, Xingzhi Guo +4
Representation learning-based recommendation models play a dominant role among recommendation techniques. However, most of the existing methods assume both historical interactions…