65 citations · 376 across the 32 of their papers we have counts for
7 papers · 1 filter
Mining Maximal Dynamic Spatial Co-Location Patterns
Xin Hu, Guoyin Wang, Jiangli Duan
A spatial co-location pattern represents a subset of spatial features whose instances are prevalently located together in a geographic space. Although many algorithms of mining spa…
Generative Adversarial Network Training is a Continual Learning Problem
Kevin J Liang, Chunyuan Li, Guoyin Wang +1
Generative Adversarial Networks (GANs) have proven to be a powerful framework for learning to draw samples from complex distributions. However, GANs are also notoriously difficult…
Sequence Generation with Guider Network
Ruiyi Zhang, Changyou Chen, Zhe Gan +5
Sequence generation with reinforcement learning (RL) has received significant attention recently. However, a challenge with such methods is the sparse-reward problem in the RL trai…
JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets
Yunchen Pu, Shuyang Dai, Zhe Gan +5
A new generative adversarial network is developed for joint distribution matching. Distinct from most existing approaches, that only learn conditional distributions, the proposed m…
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
Dinghan Shen, Guoyin Wang, Wenlin Wang +6
Many deep learning architectures have been proposed to model the compositionality in text sequences, requiring a substantial number of parameters and expensive computations. Howeve…
NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing
Dinghan Shen, Qinliang Su, Paidamoyo Chapfuwa +4
Semantic hashing has become a powerful paradigm for fast similarity search in many information retrieval systems. While fairly successful, previous techniques generally require two…
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…