Search Results for author: Zheng Dong

Found 11 papers, 6 papers with code

Spatio-Temporal-Decoupled Masked Pre-training: Benchmarked on Traffic Forecasting

1 code implementation1 Dec 2023 Haotian Gao, Renhe Jiang, Zheng Dong, Jinliang Deng, Yuxin Ma, Xuan Song

Accurate forecasting of multivariate traffic flow time series remains challenging due to substantial spatio-temporal heterogeneity and complex long-range correlative patterns.

 Ranked #1 on Traffic Prediction on PEMS-BAY (using extra training data)

Time Series Traffic Prediction

STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting

1 code implementation21 Aug 2023 Hangchen Liu, Zheng Dong, Renhe Jiang, Jiewen Deng, Jinliang Deng, Quanjun Chen, Xuan Song

With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge.

Time Series Traffic Prediction

Deep graph kernel point processes

no code implementations20 Jun 2023 Zheng Dong, Matthew Repasky, Xiuyuan Cheng, Yao Xie

Point process models are widely used for continuous asynchronous event data, where each data point includes time and additional information called "marks", which can be locations, nodes, or event types.

Point Processes

Conditional Generative Modeling for High-dimensional Marked Temporal Point Processes

no code implementations21 May 2023 Zheng Dong, Zekai Fan, Shixiang Zhu

To address this challenge, this study proposes a novel event-generation framework for modeling point processes with high-dimensional marks.

Point Processes

Spatio-temporal point processes with deep non-stationary kernels

no code implementations21 Nov 2022 Zheng Dong, Xiuyuan Cheng, Yao Xie

Another popular type of deep model for point process data is based on representing the influence kernel (rather than the intensity function) by neural networks.

Computational Efficiency Point Processes

Coupling User Preference with External Rewards to Enable Driver-centered and Resource-aware EV Charging Recommendation

1 code implementation23 Oct 2022 Chengyin Li, Zheng Dong, Nathan Fisher, Dongxiao Zhu

Electric Vehicle (EV) charging recommendation that both accommodates user preference and adapts to the ever-changing external environment arises as a cost-effective strategy to alleviate the range anxiety of private EV drivers.

Geometry-aware Two-scale PIFu Representation for Human Reconstruction

no code implementations3 Dec 2021 Zheng Dong, Ke Xu, Ziheng Duan, Hujun Bao, Weiwei Xu, Rynson W. H. Lau

Our key idea is to exploit the complementary properties of depth denoising and 3D reconstruction, for learning a two-scale PIFu representation to reconstruct high-frequency facial details and consistent bodies separately.

3D Human Reconstruction 3D Reconstruction +3

Topic Modeling Revisited: A Document Graph-based Neural Network Perspective

1 code implementation NeurIPS 2021 Dazhong Shen, Chuan Qin, Chao Wang, Zheng Dong, HengShu Zhu, Hui Xiong

To this end, in this paper, we revisit the task of topic modeling by transforming each document into a directed graph with word dependency as edges between word nodes, and develop a novel approach, namely Graph Neural Topic Model (GNTM).

Variational Inference

Neural Spectral Marked Point Processes

1 code implementation ICLR 2022 Shixiang Zhu, Haoyun Wang, Zheng Dong, Xiuyuan Cheng, Yao Xie

In this paper, we introduce a novel and general neural network-based non-stationary influence kernel with high expressiveness for handling complex discrete events data while providing theoretical performance guarantees.

Point Processes

Location-aware Single Image Reflection Removal

1 code implementation ICCV 2021 Zheng Dong, Ke Xu, Yin Yang, Hujun Bao, Weiwei Xu, Rynson W. H. Lau

It is beneficial to strong reflection detection and substantially improves the quality of reflection removal results.

Reflection Removal

Time-constrained Adaptive Influence Maximization

no code implementations6 Jan 2020 Guangmo Tong, Ruiqi Wang, Chen Ling, Zheng Dong, Xiang Li

The well-known influence maximization problem aims at maximizing the influence of one information cascade in a social network by selecting appropriate seed users prior to the diffusion process.

Social and Information Networks

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