Real-Time Traffic Speed Estimation With Graph Convolutional Generative Autoencoder Abstract: Real-time traffic speed estimation is an essential component of intelligent transportation system (ITS) ...

It uses an LSTM (Long Short-Term Memory) autoencoder model built with TensorFlow/Keras to learn normal patterns from your metrics and identify deviations. The system includes scripts for data ...

Google announced at Google I/O 2025 that it is making the AI model that powers its experimental music production app, MusicFX DJ, available via an API. The model, Lyria RealTime, is now in Google ...

Google Search Live will offer explanations, suggestions, and links based on what you see in real time via your camera.

We designed a graph-informed convolutional autoencoder called GICA to extract high-level features from the functional connectivity features. Furthermore, an attention layer based on recurrence rate ...

RAVE: Realtime Audio Variational autoEncoder Official implementation of RAVE: A variational autoencoder for fast and high-quality neural audio synthesis (article link) by Antoine Caillon and Philippe ...

In this article, a deep learning (DL) method based on autoencoder network is proposed to achieve the inverse design of phase retrieval for large-scale antenna arrays. The inverse problem between the ...

Learn how ChatGPT's Realtime API transforms app development with real-time speech processing, dynamic voices, and cost-saving features.

Compared to using PCA for dimensionality reduction, using a neural autoencoder has the big advantage that it works with source data that contains both numeric and categorical data, while PCA works ...

Data-driven soft sensors play an important role in practical processes and have been widely applied. They provide real-time prediction of quality variables and then guide production and improve ...