ABSTRACT: Accurate measurement of time-varying systematic risk exposures is essential for robust financial risk management. Conventional asset pricing models, such as the Fama-French three-factor ...

PyTorch reimplementation of "Deep Hierarchical Planning" RL framework. Features a multi-model architecture with manager-worker policies, world model, and goal autoencoder. Built with Python/PyTorch ...

Abstract: This paper presents an approach to Model Predictive Control (MPC) for nonlinear systems by combining Koopman operator theory with autoencoder networks enhanced by temporal layers for ...

Deep residual autoencoder for reconstructing and analyzing spectral data using PyTorch. Includes composite loss, UMAP visualization, and spectral diagnostics. Built for unsupervised learning on ...

AI is being rapidly adopted in edge computing. As a result, it is increasingly important to deploy machine learning models on Arm edge devices. Arm-based processors are common in embedded systems ...

Abstract: Accurate automated extraction of coseismic deformation from synthetic aperture radar (SAR) data can be challenging owing to interference from inherent atmospheric noise. Particularly, the ...