IEEE

Self-Supervised Masked Graph Autoencoder for Hyperspectral Anomaly Detection

Abstract: Hyperspectral image anomaly detection faces the challenge of difficulty in annotating anomalous targets. Autoencoder(AE)-based methods are widely used due to their excellent image ...
Scientific Research Publishing

Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J. et al. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information ...

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 ...
GitHub

Enhancing EV Cost Prediction Using AutoEncoder-Based Feature Learning

Electric Vehicle (EV) cost prediction involves analyzing complex, high-dimensional data that often contains noise, multicollinearity, and irrelevant features. Traditional regression models struggle to ...
GitHub

TorchScript based ONNX exporter: Shape Inference failure

I want to export the AutoencoderKLCosmos model to ONNX using the TorchScript-based exporter. I'm using the script below import torch from diffusers.models import AutoencoderKLCosmos dtype = ...
marktechpost

Meta AI Introduces CoCoMix: A Pretraining Framework Integrating Token Prediction with Continuous Concepts

The dominant approach to pretraining large language models (LLMs) relies on next-token prediction, which has proven effective in capturing linguistic patterns. However, this method comes with notable ...
IEEE

Deep Learning-Based Coseismic Deformation Estimation From InSAR Interferograms

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 ...