105 citations · 372 across the 39 of their papers we have counts for
9 papers · 1 filter
Disentangled Dynamic Graph Deep Generation
Wenbin Zhang, Liming Zhang, Dieter Pfoser +1
Deep generative models for graphs have exhibited promising performance in ever-increasing domains such as design of molecules (i.e, graph of atoms) and structure prediction of prot…
Graph-based Multi-hop Reasoning for Long Text Generation
Liang Zhao, Jingjing Xu, Junyang Lin +3
Long text generation is an important but challenging task.The main problem lies in learning sentence-level semantic dependencies which traditional generative models often suffer fr…
Factorized Deep Generative Models for Trajectory Generation with Spatiotemporal-Validity Constraints
Liming Zhang, Liang Zhao, Dieter Pfoser
Trajectory data generation is an important domain that characterizes the generative process of mobility data. Traditional methods heavily rely on predefined heuristics and distribu…
Tunable Subnetwork Splitting for Model-parallelism of Neural Network Training
Junxiang Wang, Zheng Chai, Yue Cheng +1
Alternating minimization methods have recently been proposed as alternatives to the gradient descent for deep neural network optimization. Alternating minimization methods can typi…
Toward Model Parallelism for Deep Neural Network based on Gradient-free ADMM Framework
Junxiang Wang, Zheng Chai, Yue Cheng +1
Alternating Direction Method of Multipliers (ADMM) has recently been proposed as a potential alternative optimizer to the Stochastic Gradient Descent(SGD) for deep learning problem…
A Systematic Survey on Deep Generative Models for Graph Generation
Xiaojie Guo, Liang Zhao
Graphs are important data representations for describing objects and their relationships, which appear in a wide diversity of real-world scenarios. As one of a critical problem in…
TG-GAN: Continuous-time Temporal Graph Generation with Deep Generative Models
Liming Zhang, Liang Zhao, Shan Qin +1
The recent deep generative models for static graphs that are now being actively developed have achieved significant success in areas such as molecule design. However, many real-wor…
Generating Tertiary Protein Structures via an Interpretative Variational Autoencoder
Xiaojie Guo, Yuanqi Du, Sivani Tadepalli +2
Much scientific enquiry across disciplines is founded upon a mechanistic treatment of dynamic systems that ties form to function. A highly visible instance of this is in molecular…
Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks
Zhiqian Chen, Fanglan Chen, Lei Zhang +7
Deep learning's success has been widely recognized in a variety of machine learning tasks, including image classification, audio recognition, and natural language processing. As an…