Posts by Collection

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Where to go? Predicting next location in IoT environment

Published in Frontiers of Computer Science, 2019

This paper is about the location prediction, using Joint Modeling of Trajectory and Signal Sequence.

Recommended citation: Lin, Hao, et al. "Where to go? Predicting next location in IoT environment." Frontiers of Computer Science 15.1 (2020): 1-13. https://link.springer.com/article/10.1007/s11704-019-9118-9

Matching Neural Network for Extreme Multi-Label Learning

Published in 4th International Conference on Artificial Intelligence Applications and Technologies (AIAAT 2020), 2020

This paper is about the Extreme Multi-Label Learning (XML), using Matching Neural Network (MNN) to learn the embeddings of feature sets and label sets.

Recommended citation: Zhao, Zhiyun, et al. "Matching Neural Network for Extreme Multi-Label Learning." Journal of Physics: Conference Series. Vol. 1642. No. 1. IOP Publishing, 2020. https://iopscience.iop.org/article/10.1088/1742-6596/1642/1/012013/meta

BoostXML: Gradient Boosting for Extreme Multi-Label Learning with Tail Labels

Published in Under Review by TNNLS, 2022

This paper is about the Extreme Multi-Label Learning (XML), using BoostXML to learn the representations of tail labels from one instance better.

Modeling Evolution on Temporal Graphs via Continuous Neighbors Aggregation

Published in Under Review by AAAI 2022, 2022

This paper is about the Dynamic Graph Representation Learning, using Continuous Neighbors Aggregation (CoNA) to learn the dynamic representations of nodes in graphs.

Talk 1 on Relevant Topic in Your Field

Published: March 01, 2012

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Tutorial 1 on Relevant Topic in Your Field

Published: March 01, 2013

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Talk 2 on Relevant Topic in Your Field

Published: February 01, 2014

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Conference Proceeding talk 3 on Relevant Topic in Your Field

Published: March 01, 2014

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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