Search Results for author: Min Shi

Found 10 papers, 1 papers with code

Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting

no code implementations16 Mar 2022 Min Shi, Hao Lu, Chen Feng, Chengxin Liu, Zhiguo Cao

In this work, we propose a similarity-aware CAC framework that jointly learns representation and similarity metric.

ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecasting

no code implementations12 Aug 2021 Yu Huang, James Li, Min Shi, Hanqi Zhuang, Xingquan Zhu, Laurent Chérubin, James VanZwieten, Yufei Tang

A spatio-temporal physics-coupled neural network (ST-PCNN) model is proposed to achieve three goals: (1) learning the underlying physics parameters, (2) transition of local information between spatio-temporal regions, and (3) forecasting future values for the dynamical system.

Physics-Coupled Spatio-Temporal Active Learning for Dynamical Systems

no code implementations11 Aug 2021 Yu Huang, Yufei Tang, Xingquan Zhu, Min Shi, Ali Muhamed Ali, Hanqi Zhuang, Laurent Cherubin

To tackle these challenges, we advocate a spatio-temporal physics-coupled neural networks (ST-PCNN) model to learn the underlying physics of the dynamical system and further couple the learned physics to assist the learning of the recurring dynamics.

Active Learning Spatio-Temporal Forecasting

Deep Attributed Network Representation Learning via Attribute Enhanced Neighborhood

no code implementations12 Apr 2021 Cong Li, Min Shi, Bo Qu, Xiang Li

In this paper, we propose a deep attributed network representation learning via attribute enhanced neighborhood (DANRL-ANE) model to improve the robustness and effectiveness of node representations.

Link Prediction Node Classification +1

Evolutionary Architecture Search for Graph Neural Networks

1 code implementation21 Sep 2020 Min Shi, David A. Wilson, Xingquan Zhu, Yu Huang, Yuan Zhuang, Jianxun Liu, Yufei Tang

In particular, Neural Architecture Search (NAS) has seen significant attention throughout the AutoML research community, and has pushed forward the state-of-the-art in a number of neural models to address grid-like data such as texts and images.

Neural Architecture Search Representation Learning

Deep Line Art Video Colorization with a Few References

no code implementations24 Mar 2020 Min Shi, Jia-Qi Zhang, Shu-Yu Chen, Lin Gao, Yu-Kun Lai, Fang-Lue Zhang

The color transform network takes the target line art images as well as the line art and color images of one or more reference images as input, and generates corresponding target color images.

Colorization

Feature-Attention Graph Convolutional Networks for Noise Resilient Learning

no code implementations26 Dec 2019 Min Shi, Yufei Tang, Xingquan Zhu, Jianxun Liu

By using spectral-based graph convolution aggregation process, each node is allowed to concentrate more on the most determining neighborhood features aligned with the corresponding learning task.

Feature Importance

Multi-Label Graph Convolutional Network Representation Learning

no code implementations26 Dec 2019 Min Shi, Yufei Tang, Xingquan Zhu, Jianxun Liu

The multi-label network nodes not only have multiple labels for each node, such labels are often highly correlated making existing methods ineffective or fail to handle such correlation for node representation learning.

Multi-Label Classification Node Classification +1

DOT: Gene-set analysis by combining decorrelated association statistics

no code implementations5 Jun 2019 Olga A Vsevolozhskaya, Min Shi, Fengjiao Hu, Dmitri V Zaykin

Historically, the majority of statistical association methods have been designed assuming availability of SNP-level information.

Genomics Applications

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