122 citations · 436 across the 20 of their papers we have counts for
10 papers · 1 filter
Interpretable Deep Graph Generation with Node-Edge Co-Disentanglement
Xiaojie Guo, Liang Zhao, Zhao Qin +3
Disentangled representation learning has recently attracted a significant amount of attention, particularly in the field of image representation learning. However, learning the dis…
Deep Multi-attributed Graph Translation with Node-Edge Co-evolution
Xiaojie Guo, Liang Zhao, Cameron Nowzari +3
Generalized from image and language translation, graph translation aims to generate a graph in the target domain by conditioning an input graph in the source domain. This promising…
Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks
Farnaz Behnia, Ali Mirzaeian, Mohammad Sabokrou +6
In this paper, we propose Code-Bridged Classifier (CBC), a framework for making a Convolutional Neural Network (CNNs) robust against adversarial attacks without increasing or even…
Efficient Global String Kernel with Random Features: Beyond Counting Substructures
Lingfei Wu, Ian En-Hsu Yen, Siyu Huo +5
Analysis of large-scale sequential data has been one of the most crucial tasks in areas such as bioinformatics, text, and audio mining. Existing string kernels, however, either (i)…
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding
Lingfei Wu, Ian En-Hsu Yen, Zhen Zhang +5
Graph kernels are widely used for measuring the similarity between graphs. Many existing graph kernels, which focus on local patterns within graphs rather than their global propert…
TITAN: A Spatiotemporal Feature Learning Framework for Traffic Incident Duration Prediction
Kaiqun Fu, Taoran Ji, Liang Zhao +1
Critical incident stages identification and reasonable prediction of traffic incident duration are essential in traffic incident management. In this paper, we propose a traffic inc…
Multi-stage Deep Classifier Cascades for Open World Recognition
Xiaojie Guo, Amir Alipour-Fanid, Lingfei Wu +4
At present, object recognition studies are mostly conducted in a closed lab setting with classes in test phase typically in training phase. However, real-world problem is far more…
DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums
Yuyang Gao, Lingfei Wu, Houman Homayoun +1
Online health communities such as the online breast cancer forum enable patients (i.e., users) to interact and help each other within various subforums, which are subsections of th…
Robust Regression via Online Feature Selection under Adversarial Data Corruption
Xuchao Zhang, Shuo Lei, Liang Zhao +2
The presence of data corruption in user-generated streaming data, such as social media, motivates a new fundamental problem that learns reliable regression coefficient when feature…
A Generic Framework for Interesting Subspace Cluster Detection in Multi-attributed Networks
Feng Chen, Baojian Zhou, Adil Alim +1
Detection of interesting (e.g., coherent or anomalous) clusters has been studied extensively on plain or univariate networks, with various applications. Recently, algorithms have b…