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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence / EAAI-20

Calibrated Stochastic Gradient Descent for Convolutional Neural Networks

February 1, 2023

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Authors

Li’an Zhuo

Beihang University


Baochang Zhang

Beihang University


Chen Chen

University of North Carolina at Charlotte


Qixiang Ye

University of Chinese Academy of Sciences


Jianzhuang Liu

Huawei Technologies Company, Ltd.


David Doermann

State University of New York at Buffalo


DOI:

10.1609/aaai.v33i01.33019348


Abstract:

In stochastic gradient descent (SGD) and its variants, the optimized gradient estimators may be as expensive to compute as the true gradient in many scenarios. This paper introduces a calibrated stochastic gradient descent (CSGD) algorithm for deep neural network optimization. A theorem is developed to prove that an unbiased estimator for the network variables can be obtained in a probabilistic way based on the Lipschitz hypothesis. Our work is significantly distinct from existing gradient optimization methods, by providing a theoretical framework for unbiased variable estimation in the deep learning paradigm to optimize the model parameter calculation. In particular, we develop a generic gradient calibration layer which can be easily used to build convolutional neural networks (CNNs). Experimental results demonstrate that CNNs with our CSGD optimization scheme can improve the stateof-the-art performance for natural image classification, digit recognition, ImageNet object classification, and object detection tasks. This work opens new research directions for developing more efficient SGD updates and analyzing the backpropagation algorithm.

Topics: AAAI

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Li’an Zhuo||Baochang Zhang||Chen Chen||Qixiang Ye||Jianzhuang Liu||David Doermann Calibrated Stochastic Gradient Descent for Convolutional Neural Networks Proceedings of the AAAI Conference on Artificial Intelligence (2019) 9348-9355.

Li’an Zhuo||Baochang Zhang||Chen Chen||Qixiang Ye||Jianzhuang Liu||David Doermann Calibrated Stochastic Gradient Descent for Convolutional Neural Networks AAAI 2019, 9348-9355.

Li’an Zhuo||Baochang Zhang||Chen Chen||Qixiang Ye||Jianzhuang Liu||David Doermann (2019). Calibrated Stochastic Gradient Descent for Convolutional Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 9348-9355.

Li’an Zhuo||Baochang Zhang||Chen Chen||Qixiang Ye||Jianzhuang Liu||David Doermann. Calibrated Stochastic Gradient Descent for Convolutional Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence 2019 p.9348-9355.

Li’an Zhuo||Baochang Zhang||Chen Chen||Qixiang Ye||Jianzhuang Liu||David Doermann. 2019. Calibrated Stochastic Gradient Descent for Convolutional Neural Networks. "Proceedings of the AAAI Conference on Artificial Intelligence". 9348-9355.

Li’an Zhuo||Baochang Zhang||Chen Chen||Qixiang Ye||Jianzhuang Liu||David Doermann. (2019) "Calibrated Stochastic Gradient Descent for Convolutional Neural Networks", Proceedings of the AAAI Conference on Artificial Intelligence, p.9348-9355

Li’an Zhuo||Baochang Zhang||Chen Chen||Qixiang Ye||Jianzhuang Liu||David Doermann, "Calibrated Stochastic Gradient Descent for Convolutional Neural Networks", AAAI, p.9348-9355, 2019.

Li’an Zhuo||Baochang Zhang||Chen Chen||Qixiang Ye||Jianzhuang Liu||David Doermann. "Calibrated Stochastic Gradient Descent for Convolutional Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence, 2019, p.9348-9355.

Li’an Zhuo||Baochang Zhang||Chen Chen||Qixiang Ye||Jianzhuang Liu||David Doermann. "Calibrated Stochastic Gradient Descent for Convolutional Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence, (2019): 9348-9355.

Li’an Zhuo||Baochang Zhang||Chen Chen||Qixiang Ye||Jianzhuang Liu||David Doermann. Calibrated Stochastic Gradient Descent for Convolutional Neural Networks. AAAI[Internet]. 2019[cited 2023]; 9348-9355.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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