Optimizing Hyperparameters in CNNs using Bilevel Programming in Time Series Data
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Abstract (arXiv)
Hyperparameter optimization has remained a central topic within the machine learning community due to its ability to produce state-of-the-art results. With the recent interest growing in the usage of CNNs for time series prediction, we propose the notion of optimizing Hyperparameters in CNNs for the purpose of time series prediction. In this position paper, we give away the idea of modeling the concerned hyperparameter optimization problem using bilevel programming.
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References · 6
- 2016Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimizationcited by 688
- 2018Bilevel Programming for Hyperparameter Optimization and Meta-Learningcited by 403
- 2015Gradient-based Hyperparameter Optimization through Reversible Learningcited by 357
- 2017Conditional Time Series Forecasting with Convolutional Neural Networkscited by 121
- 2016Multi-Scale Convolutional Neural Networks for Time Series Classificationcited by 99
- 2019Multivariate Time Series Classification using Dilated Convolutional Neural Networkcited by 9
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