Correlation refers to statistical measures that quantify the relationship between different variables, such as the correlation between input features and the target output in a machine learning model. Understanding and managing correlations in neural networks are crucial for effective model training, interpretation, and generalization. High input feature correlations may impact model interpretability and generalization to new data, while correlations within hidden layers can influence the learning process. Addressing issues related to correlation, such as feature engineering, regularization techniques, or architectural modifications, can contribute to building more robust and accurate neural network models.
We experimentally demonstrated the real-time operation of a photonic neuron with a self-connection, a prerequisite for integrated recurrent neural networks (RNNs). After studying two applications, we propose a photonics-assisted platform for time series prediction and classification.
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