45 citations · 153 across the 14 of their papers we have counts for
15 papers
GANSpiration: Balancing Targeted and Serendipitous Inspiration in User Interface Design with Style-Based Generative Adversarial Network
Mohammad Amin Mozaffari, Xinyuan Zhang, Jinghui Cheng +1
Inspiration from design examples plays a crucial role in the creative process of user interface design. However, current tools and techniques that support inspiration usually only…
Cognitive Semantic Communication Systems Driven by Knowledge Graph
Fuhui Zhou, Yihao Li, Xinyuan Zhang +3
Semantic communication is envisioned as a promising technique to break through the Shannon limit. However, the existing semantic communication frameworks do not involve inference a…
GIU-GANs: Global Information Utilization for Generative Adversarial Networks
Yongqi Tian, Xueyuan Gong, Jialin Tang +3
In recent years, with the rapid development of artificial intelligence, image generation based on deep learning has dramatically advanced. Image generation based on Generative Adve…
KDCTime: Knowledge Distillation with Calibration on InceptionTime for Time-series Classification
Xueyuan Gong, Yain-Whar Si, Yongqi Tian +3
Time-series classification approaches based on deep neural networks are easy to be overfitting on UCR datasets, which is caused by the few-shot problem of those datasets. Therefore…
Unsupervised Abstractive Dialogue Summarization for Tete-a-Tetes
Xinyuan Zhang, Ruiyi Zhang, Manzil Zaheer +1
High-quality dialogue-summary paired data is expensive to produce and domain-sensitive, making abstractive dialogue summarization a challenging task. In this work, we propose the f…
Dynamic Embedding on Textual Networks via a Gaussian Process
Pengyu Cheng, Yitong Li, Xinyuan Zhang +3
Textual network embedding aims to learn low-dimensional representations of text-annotated nodes in a graph. Prior work in this area has typically focused on fixed graph structures;…
Improving Textual Network Learning with Variational Homophilic Embeddings
Wenlin Wang, Chenyang Tao, Zhe Gan +7
The performance of many network learning applications crucially hinges on the success of network embedding algorithms, which aim to encode rich network information into low-dimensi…
Learning Compressed Sentence Representations for On-Device Text Processing
Dinghan Shen, Pengyu Cheng, Dhanasekar Sundararaman +5
Vector representations of sentences, trained on massive text corpora, are widely used as generic sentence embeddings across a variety of NLP problems. The learned representations a…
Syntax-Infused Variational Autoencoder for Text Generation
Xinyuan Zhang, Yi Yang, Siyang Yuan +2
We present a syntax-infused variational autoencoder (SIVAE), that integrates sentences with their syntactic trees to improve the grammar of generated sentences. Distinct from exist…
Improving Textual Network Embedding with Global Attention via Optimal Transport
Liqun Chen, Guoyin Wang, Chenyang Tao +6
Constituting highly informative network embeddings is an important tool for network analysis. It encodes network topology, along with other useful side information, into low-dimens…
Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment
Dinghan Shen, Xinyuan Zhang, Ricardo Henao +1
Network embeddings, which learn low-dimensional representations for each vertex in a large-scale network, have received considerable attention in recent years. For a wide range of…
Joint Embedding of Words and Labels for Text Classification
Guoyin Wang, Chunyuan Li, Wenlin Wang +5
Word embeddings are effective intermediate representations for capturing semantic regularities between words, when learning the representations of text sequences. We propose to vie…
Diffusion Maps for Textual Network Embedding
Xinyuan Zhang, Yitong Li, Dinghan Shen +1
Textual network embedding leverages rich text information associated with the network to learn low-dimensional vectorial representations of vertices. Rather than using typical natu…
Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration
Xinyuan Zhang, Xin Yuan, Lawrence Carin
Low-rank signal modeling has been widely leveraged to capture non-local correlation in image processing applications. We propose a new method that employs low-rank tensor factor an…
Multi-Label Learning from Medical Plain Text with Convolutional Residual Models
Xinyuan Zhang, Ricardo Henao, Zhe Gan +2
Predicting diagnoses from Electronic Health Records (EHRs) is an important medical application of multi-label learning. We propose a convolutional residual model for multi-label cl…