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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Compact Autoregressive Network

February 1, 2023

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Authors

Di Wang

The University of Hong Kong


Feiqing Huang

The University of Hong Kong


Jingyu Zhao

The University of Hong Kong


Guodong Li

The University of Hong Kong


Guangjian Tian

Huawei Noah's Ark Lab


DOI:

10.1609/aaai.v34i04.6079


Abstract:

Autoregressive networks can achieve promising performance in many sequence modeling tasks with short-range dependence. However, when handling high-dimensional inputs and outputs, the massive amount of parameters in the network leads to expensive computational cost and low learning efficiency. The problem can be alleviated slightly by introducing one more narrow hidden layer to the network, but the sample size required to achieve a certain training error is still substantial. To address this challenge, we rearrange the weight matrices of a linear autoregressive network into a tensor form, and then make use of Tucker decomposition to represent low-rank structures. This leads to a novel compact autoregressive network, called Tucker AutoRegressive (TAR) net. Interestingly, the TAR net can be applied to sequences with long-range dependence since the dimension along the sequential order is reduced. Theoretical studies show that the TAR net improves the learning efficiency, and requires much fewer samples for model training. Experiments on synthetic and real-world datasets demonstrate the promising performance of the proposed compact network.

Topics: AAAI

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HOW TO CITE:

Di Wang||Feiqing Huang||Jingyu Zhao||Guodong Li||Guangjian Tian Compact Autoregressive Network Proceedings of the AAAI Conference on Artificial Intelligence (2020) 6145-6152.

Di Wang||Feiqing Huang||Jingyu Zhao||Guodong Li||Guangjian Tian Compact Autoregressive Network AAAI 2020, 6145-6152.

Di Wang||Feiqing Huang||Jingyu Zhao||Guodong Li||Guangjian Tian (2020). Compact Autoregressive Network. Proceedings of the AAAI Conference on Artificial Intelligence, 6145-6152.

Di Wang||Feiqing Huang||Jingyu Zhao||Guodong Li||Guangjian Tian. Compact Autoregressive Network. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.6145-6152.

Di Wang||Feiqing Huang||Jingyu Zhao||Guodong Li||Guangjian Tian. 2020. Compact Autoregressive Network. "Proceedings of the AAAI Conference on Artificial Intelligence". 6145-6152.

Di Wang||Feiqing Huang||Jingyu Zhao||Guodong Li||Guangjian Tian. (2020) "Compact Autoregressive Network", Proceedings of the AAAI Conference on Artificial Intelligence, p.6145-6152

Di Wang||Feiqing Huang||Jingyu Zhao||Guodong Li||Guangjian Tian, "Compact Autoregressive Network", AAAI, p.6145-6152, 2020.

Di Wang||Feiqing Huang||Jingyu Zhao||Guodong Li||Guangjian Tian. "Compact Autoregressive Network". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.6145-6152.

Di Wang||Feiqing Huang||Jingyu Zhao||Guodong Li||Guangjian Tian. "Compact Autoregressive Network". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 6145-6152.

Di Wang||Feiqing Huang||Jingyu Zhao||Guodong Li||Guangjian Tian. Compact Autoregressive Network. AAAI[Internet]. 2020[cited 2023]; 6145-6152.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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