1 code implementation • 6 Oct 2021 • Yan Xiao, Yun Lin, Ivan Beschastnikh, Changsheng Sun, David S. Rosenblum, Jin Song Dong
However, inputs may deviate from the training dataset distribution in real deployments.
no code implementations • 29 Apr 2020 • Zekun Tong, Yuxuan Liang, Changsheng Sun, David S. Rosenblum, Andrew Lim
Graph Convolutional Networks (GCNs) have been widely used due to their outstanding performance in processing graph-structured data.
1 code implementation • 28 Feb 2020 • Yuxuan Liang, Kun Ouyang, Yiwei Wang, Ye Liu, Junbo Zhang, Yu Zheng, David S. Rosenblum
This framework consists of three parts: 1) a local feature extraction module to learn representations for each region; 2) a global context module to extract global contextual priors and upsample them to generate the global features; and 3) a region-specific predictor based on tensor decomposition to provide customized predictions for each region, which is very parameter-efficient compared to previous methods.
1 code implementation • 5 Feb 2020 • Kun Ouyang, Yuxuan Liang, Ye Liu, Zekun Tong, Sijie Ruan, Yu Zheng, David S. Rosenblum
To tackle these issues, we develop a model entitled UrbanFM which consists of two major parts: 1) an inference network to generate fine-grained flow distributions from coarse-grained inputs that uses a feature extraction module and a novel distributional upsampling module; 2) a general fusion subnet to further boost the performance by considering the influence of different external factors.
4 code implementations • 13 Mar 2019 • Ye Liu, Hui Li, Alberto Garcia-Duran, Mathias Niepert, Daniel Onoro-Rubio, David S. Rosenblum
We present MMKG, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between pairs of KGs.
1 code implementation • 6 Feb 2019 • Yuxuan Liang, Kun Ouyang, Lin Jing, Sijie Ruan, Ye Liu, Junbo Zhang, David S. Rosenblum, Yu Zheng
In this paper, we aim to infer the real-time and fine-grained crowd flows throughout a city based on coarse-grained observations.
Ranked #1 on Fine-Grained Urban Flow Inference on TaxiBJ-P4
no code implementations • 4 Jan 2017 • Li Liu, Yongzhong Yang, Lakshmi Narasimhan Govindarajan, Shu Wang, Bin Hu, Li Cheng, David S. Rosenblum
We propose in this paper an atomic action-based Bayesian model that constructs Allen's interval relation networks to characterize complex activities with structural varieties in a probabilistic generative way: By introducing latent variables from the Chinese restaurant process, our approach is able to capture all possible styles of a particular complex activity as a unique set of distributions over atomic actions and relations.
no code implementations • 7 Nov 2016 • Ye Liu, Liqiang Nie, Lei Han, Luming Zhang, David S. Rosenblum
As compared to simple actions, activities are much more complex, but semantically consistent with a human's real life.