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Found 9 papers, 2 papers with code
Data-driven Option Pricing
We propose an innovative data-driven option pricing methodology that relies exclusively on the dataset of historical underlying asset prices.
Learning Merton's Strategies in an Incomplete Market: Recursive Entropy Regularization and Biased Gaussian Exploration
We study Merton's expected utility maximization problem in an incomplete market, characterized by a factor process in addition to the stock price process, where all the model primitives are unknown.
A Fast Fourier Convolutional Deep Neural Network For Accurate and Explainable Discrimination Of Wheat Yellow Rust And Nitrogen Deficiency From Sentinel-2 Time-Series Data
Specifically, unlike the existing CNN models, the main components of the proposed model include: 1) a fast Fourier convolutional block, a newly fast Fourier transformation kernel as the basic perception unit, to substitute the traditional convolutional kernel to capture both local and global responses to plant stress in various time-scale and improve computing efficiency with reduced learning parameters in Fourier domain; 2) Capsule Feature Encoder to encapsulate the extracted features into a series of vector features to represent part-to-whole relationship with the hierarchical structure of the host-stress interactions of the specific stress.
Multi-task Meta Label Correction for Time Series Prediction
To address the above issues, we create a label correction method to time series data with meta-learning under a multi-task framework.
Regularized Primitive Graph Learning for Unified Vector Mapping
Our key idea is using primitive graph as a unified representation of vector maps and formulating shape regularization and topology reconstruction as primitive graph reconstruction problems that can be solved in the same framework.
Primitive Graph Learning for Unified Vector Mapping
Then, we convert vector shape prediction, regularization, and topology reconstruction into a unique primitive graph learning problem.
Learning effective dynamics from data-driven stochastic systems
Multiscale stochastic dynamical systems have been widely adopted to a variety of scientific and engineering problems due to their capability of depicting complex phenomena in many real world applications.
Memory-Gated Recurrent Networks
The essence of multivariate sequential learning is all about how to extract dependencies in data.
Real-Time Fault Detection and Process Control Based on Multi-channel Sensor Data Fusion
Sensor signals acquired in the industrial process contain rich information which can be analyzed to facilitate effective monitoring of the process, early detection of system anomalies, quick diagnosis of fault root causes, and intelligent system design and control.
