activity
20202022
most citedPredicting Temporal Sets with Deep Neural Networks

54 citations · 106 across the 10 of their papers we have counts for

collaborators

10 papers

cs.LG20222 cited

GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation

Han Huang, Leilei Sun, Bowen Du +2

Graph generative models have broad applications in biology, chemistry and social science. However, modelling and understanding the generative process of graphs is challenging due t…

cs.AI20221 cited

Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning

Dongjie Wang, Lingfei Wu, Denghui Zhang +3

The essential task of urban planning is to generate the optimal land-use configuration of a target area. However, traditional urban planning is time-consuming and labor-intensive.…

cs.AI2022

Automated Urban Planning aware Spatial Hierarchies and Human Instructions

Dongjie Wang, Kunpeng Liu, Yanyong Huang +3

Traditional urban planning demands urban experts to spend considerable time and effort producing an optimal urban plan under many architectural constraints. The remarkable imaginat…

cs.CV2021

Deep Human-guided Conditional Variational Generative Modeling for Automated Urban Planning

Dongjie Wang, Kunpeng Liu, Pauline Johnson +3

Urban planning designs land-use configurations and can benefit building livable, sustainable, safe communities. Inspired by image generation, deep urban planning aims to leverage d…

cs.LG20212 cited

Analysis for full face mechanical behaviors through spatial deduction model with real-time monitoring data

Xuyan Tan, Yuhang Wang, Bowen Du +4

Mechanical analysis for the full face of tunnel structure is crucial to maintain stability, which is a challenge in classical analytical solutions and data analysis. Along this lin…

cs.CL202114 cited

LightXML: Transformer with Dynamic Negative Sampling for High-Performance Extreme Multi-label Text Classification

Ting Jiang, Deqing Wang, Leilei Sun +3

Extreme Multi-label text Classification (XMC) is a task of finding the most relevant labels from a large label set. Nowadays deep learning-based methods have shown significant succ…