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Understanding Time Series Anomaly State Detection through One-Class Classification
Therefore, in this article, we try to re-understand and define the time series anomaly detection problem through OCC, which we call 'time series anomaly state detection problem'.
Understanding the Initial Condensation of Convolutional Neural Networks
Previous research has shown that fully-connected networks with small initialization and gradient-based training methods exhibit a phenomenon known as condensation during training.
Empirical Phase Diagram for Three-layer Neural Networks with Infinite Width
Through experiments under three-layer condition, our phase diagram suggests a complicated dynamical regimes consisting of three possible regimes, together with their mixture, for deep NNs and provides a guidance for studying deep NNs in different initialization regimes, which reveals the possibility of completely different dynamics emerging within a deep NN for its different layers.
Dropout in Training Neural Networks: Flatness of Solution and Noise Structure
It is important to understand how the popular regularization method dropout helps the neural network training find a good generalization solution.
Towards Understanding the Condensation of Neural Networks at Initial Training
Our theoretical analysis confirms experiments for two cases, one is for the activation function of multiplicity one with arbitrary dimension input, which contains many common activation functions, and the other is for the layer with one-dimensional input and arbitrary multiplicity.
Deep frequency principle towards understanding why deeper learning is faster
Due to the well-studied frequency principle, i. e., deep neural networks learn lower frequency functions faster, the deep frequency principle provides a reasonable explanation to why deeper learning is faster.
