GitHub

Detects anomalies in structured logs/events using unsupervised machine learning (e.g. neural-network autoencoder with embeddings and/or isolation forest).

Autoencoder score mapping: Reconstruction MSE is converted to a percentile rank within the baseline MSEs from the last retrain. The raw percentile is remapped through a piecewise-linear function so ...
IEEE

Signal-Guided Masked Autoencoder for Wireless Positioning With Limited Labeled Samples

Abstract: In this paper, a signal-guided masked autoencoder (S-MAE) based semi-supervised learning framework is proposed for high-precision positioning with limited labeled channel impulse response ...
The Hacker News

PyTorch Lightning and Intercom-client Hit in Supply Chain Attacks to Steal Credentials

In yet another software supply chain attack, threat actors have managed to compromise the popular Python package Lightning to push two malicious versions to conduct credential theft. As of writing, ...
GitHub

CNN-AutoEncoder in pytorch

This is a pytorch implementation of the Muti-task Learning using CNN + AutoEncoder. Cifar10 is available for the datas et by default. You can also use your own dataset. epoch,train loss,train accuracy ...
Reuters

Google works to erode Nvidia's software advantage with Meta's help

Google's TorchTPU aims to enhance TPU compatibility with PyTorch Google seeks to help AI developers reduce reliance on Nvidia's CUDA ecosystem TorchTPU initiative is part of Google's plan to attract ...
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 ...
IEEE

Prior Distribution Guided Gaussian Mixture Variational Autoencoder (PDGM-VAE) for Image Generation

Abstract: Variational Autoencoder(VAE) combines the ideas of autoencoders and variational inference, introducing the concept of latent space and variational inference to endow autoencoders to generate ...
VentureBeat

DeepMind makes big jump toward interpreting LLMs with sparse autoencoders

Large language models (LLMs) have made remarkable progress in recent years. But understanding how they work remains a challenge and scientists at artificial intelligence labs are trying to peer into ...