217 citations · 2.1k across the 107 of their papers we have counts for
16 papers · 1 filter
DQ Scheduler: Deep Reinforcement Learning Based Controller Synchronization in Distributed SDN
Ziyao Zhang, Liang Ma, Konstantinos Poularakis +2
In distributed software-defined networks (SDN), multiple physical SDN controllers, each managing a network domain, are implemented to balance centralized control, scalability and r…
Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification
Qi Lei, Lingfei Wu, Pin-Yu Chen +3
Adversarial examples are carefully constructed modifications to an input that completely change the output of a classifier but are imperceptible to humans. Despite these successful…
The Cinderella Complex: Word Embeddings Reveal Gender Stereotypes in Movies and Books
Huimin Xu, Zhang Zhang, Lingfei Wu +1
Our analysis of thousands of movies and books reveals how these cultural products weave stereotypical gender roles into morality tales and perpetuate gender inequality through stor…
Word Mover's Embedding: From Word2Vec to Document Embedding
Lingfei Wu, Ian E. H. Yen, Kun Xu +5
While the celebrated Word2Vec technique yields semantically rich representations for individual words, there has been relatively less success in extending to generate unsupervised…
Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability
Lingfei Wu, Ian E. H. Yen, Jie Chen +1
Kernel method has been developed as one of the standard approaches for nonlinear learning, which however, does not scale to large data set due to its quadratic complexity in the nu…
Random Warping Series: A Random Features Method for Time-Series Embedding
Lingfei Wu, Ian En-Hsu Yen, Jinfeng Yi +3
Time series data analytics has been a problem of substantial interests for decades, and Dynamic Time Warping (DTW) has been the most widely adopted technique to measure dissimilari…
SQL-to-Text Generation with Graph-to-Sequence Model
Kun Xu, Lingfei Wu, Zhiguo Wang +2
Previous work approaches the SQL-to-text generation task using vanilla Seq2Seq models, which may not fully capture the inherent graph-structured information in SQL query. In this p…
Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model
Kun Xu, Lingfei Wu, Zhiguo Wang +3
Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information su…
Quantized Densely Connected U-Nets for Efficient Landmark Localization
Zhiqiang Tang, Xi Peng, Shijie Geng +3
In this paper, we propose quantized densely connected U-Nets for efficient visual landmark localization. The idea is that features of the same semantic meanings are globally reused…
Social Connection Induces Cultural Contraction: Evidence from Hyperbolic Embeddings of Social and Semantic Networks
Lingfei Wu, Linzhuo Li, James Evans
Research has repeatedly demonstrated the influence of social connection and communication on convergence in cultural tastes, opinions and ideas. Here we review recent studies and c…
Deep Graph Translation
Xiaojie Guo, Lingfei Wu, Liang Zhao
Inspired by the tremendous success of deep generative models on generating continuous data like image and audio, in the most recent year, few deep graph generative models have been…
Financial Forecasting and Analysis for Low-Wage Workers
Wenyu Zhang, Raya Horesh, Karthikeyan N. Ramamurthy +4
Despite the plethora of financial services and products on the market nowadays, there is a lack of such services and products designed especially for the low-wage population. Appro…
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications
Pin-Yu Chen, Lingfei Wu, Sijia Liu +1
The von Neumann graph entropy (VNGE) facilitates measurement of information divergence and distance between graphs in a graph sequence. It has been successfully applied to various…
Scalable Spectral Clustering Using Random Binning Features
Lingfei Wu, Pin-Yu Chen, Ian En-Hsu Yen +3
Spectral clustering is one of the most effective clustering approaches that capture hidden cluster structures in the data. However, it does not scale well to large-scale problems d…
Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks
Kun Xu, Lingfei Wu, Zhiguo Wang +3
The celebrated Sequence to Sequence learning (Seq2Seq) technique and its numerous variants achieve excellent performance on many tasks. However, many machine learning tasks have in…
D2KE: From Distance to Kernel and Embedding
Lingfei Wu, Ian En-Hsu Yen, Fangli Xu +2
For many machine learning problem settings, particularly with structured inputs such as sequences or sets of objects, a distance measure between inputs can be specified more natura…