A Generative Model for Sparse Hyperparameter Determination
Zhiqiang Wan,Haibo He,Bo Tang +2 more
TLDR
Experimental results and comparative studies over numerous datasets demonstrate the effectiveness of the generative model derived to determine the sparse hyperparameter effectively and efficiently.Abstract:
Sparse autoencoder is an unsupervised feature extractor and has been widely used in the machine learning and data mining community. However, a sparse hyperparameter has to be determined to balance the trade-off between the reconstruction error and the sparsity of sparse autoencoder. Traditional sparse hyperparameter determination method is time-consuming, especially when the dataset is large. In this paper, we derive a generative model for sparse autoencoder. Based on this model, we derive a formulation to determine the sparse hyperparameter effectively and efficiently. The relationship between the sparse hyperparameter and the average activation of sparse autoencoder hidden units is also presented in this paper. Experimental results and comparative studies over numerous datasets demonstrate the effectiveness of our method to determine the sparse hyperparameter.read more
Citations
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Constrained EV Charging Scheduling Based on Safe Deep Reinforcement Learning
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TL;DR: A deep reinforcement learning based approach has been proposed to solve this residential energy management problem and can directly learn the optimal energy management strategy based on reinforcement learning.
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