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 ...
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 ...
Researchers at Sandia National Laboratories have developed AI algorithms to detect physical problems, cyberattacks and both at the same time within the grid. “As more disturbances occur, whether from ...
The choice between PyTorch and TensorFlow remains one of the most debated decisions in AI development. Both frameworks have evolved dramatically since their inception, converging in some areas while ...
Electric Vehicle (EV) cost prediction involves analyzing complex, high-dimensional data that often contains noise, multicollinearity, and irrelevant features. Traditional regression models struggle to ...
Meta has introduced KernelLLM, an 8-billion-parameter language model fine-tuned from Llama 3.1 Instruct, aimed at automating the translation of PyTorch modules into efficient Triton GPU kernels. This ...
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 = ...
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 ...
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