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Found 5 papers, 1 papers with code
Overnight GARCH-Itô Volatility Models
Various parametric volatility models for financial data have been developed to incorporate high-frequency realized volatilities and better capture market dynamics.
SGD Distributional Dynamics of Three Layer Neural Networks
In this paper, we seek to extend the mean field results of Mei et al. (2018) from two-layer neural networks with one hidden layer to three-layer neural networks with two hidden layers.
Optimal High-order Tensor SVD via Tensor-Train Orthogonal Iteration
This paper studies a general framework for high-order tensor SVD.
How Many Factors Influence Minima in SGD?
The influencing factors identified in the literature include learning rate, batch size, Hessian, and gradient covariance, and stochastic differential equations are used to model SGD and establish the relationships among these factors for characterizing minima found by SGD.
Asymptotic Analysis via Stochastic Differential Equations of Gradient Descent Algorithms in Statistical and Computational Paradigms
We establish gradient flow central limit theorems to describe the limiting dynamic behaviors of these computational algorithms and the large-sample performances of the related statistical procedures, as the number of algorithm iterations and data size both go to infinity, where the gradient flow central limit theorems are governed by some linear ordinary or stochastic differential equations like time-dependent Ornstein-Uhlenbeck processes.
