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Deep Neural Networks for Image-Based Dietary Assessment
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ASD+M: Automatic parameter tuning in stochastic optimization and on-line learning.

Paweł Wawrzyński1

  • 1Warsaw University of Technology, Institute of Computer Science, Nowowiejska 15/19, 00-665 Warsaw, Poland.

Neural Networks : the Official Journal of the International Neural Network Society
|September 27, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive method for the momentum algorithm in stochastic optimization. The new approach optimizes coefficients during operation, making on-line learning practically parameter-free and enhancing performance in neural networks.

Keywords:
Classic momentumDeep learningLearning rateOn-line learningStep-sizeStochastic gradient descent

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Area of Science:

  • Machine Learning
  • Optimization Algorithms

Background:

  • The classic momentum algorithm is a cornerstone of stochastic optimization.
  • Determining optimal coefficients for this algorithm often requires prior knowledge or extensive tuning.
  • This poses a challenge for real-world applications, especially in dynamic environments.

Purpose of the Study:

  • To introduce a novel method for dynamically adjusting coefficients of the momentum algorithm.
  • To eliminate the need for preliminary knowledge of the optimization problem.
  • To enhance the practicality and applicability of on-line learning in neural networks.

Main Methods:

  • A new method is proposed to adjust momentum algorithm coefficients during operation.
  • The method is applied to on-line learning tasks in feed-forward neural networks.
  • Experimental validation includes deep auto-encoders to assess performance.

Main Results:

  • The adaptive method significantly outperforms fixed coefficients in on-line learning.
  • It effectively removes the need to determine critical, performance-influencing coefficients.
  • The method demonstrates low sensitivity to its own, easily determined coefficients.

Conclusions:

  • The proposed method transforms on-line learning into a practically parameter-free process.
  • This significantly broadens the potential applications of adaptive optimization technologies.
  • It offers a more robust and user-friendly approach to stochastic optimization.