activity
20182022
most citedGANSpiration: Balancing Targeted and Serendipitous Inspiration in User Interface Design with Style-Based Generative Adversarial Network

33 citations · 63 across the 7 of their papers we have counts for

collaborators

12 papers

cs.HC202233 cited

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…

cs.AI2022

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…

cs.CL20202 cited

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…

cs.LG2019

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;…

cs.LG20199 cited

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…

cs.CL201910 cited

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…