Search Results for author: David S. Rosenblum

Found 8 papers, 5 papers with code

Directed Graph Convolutional Network

no code implementations29 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.

Revisiting Convolutional Neural Networks for Citywide Crowd Flow Analytics

1 code implementation28 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.

Tensor Decomposition

Fine-Grained Urban Flow Inference

1 code implementation5 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.

Fine-Grained Urban Flow Inference

MMKG: Multi-Modal Knowledge Graphs

4 code implementations13 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.

Knowledge Graphs Link Prediction

UrbanFM: Inferring Fine-Grained Urban Flows

1 code implementation6 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.

Fine-Grained Urban Flow Inference

An Interval-Based Bayesian Generative Model for Human Complex Activity Recognition

no code implementations4 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.

Activity Recognition

Action2Activity: Recognizing Complex Activities from Sensor Data

no code implementations7 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.

Action Recognition Multi-Task Learning +1

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