The Temporal Fusion Transformer model provides near-real-time insights into sintering temperatures, addressing critical challenges in steelmaking processes.

Researchers at New York University have developed a new architecture for diffusion models that improves the semantic representation of the images they generate. “Diffusion Transformer with ...

ABSTRACT: The rapid growth of unlabeled time-series data in domains such as wireless communications, radar, biomedical engineering, and the Internet of Things (IoT) has driven advancements in ...

ABSTRACT: The rapid growth of unlabeled time-series data in domains such as wireless communications, radar, biomedical engineering, and the Internet of Things (IoT) has driven advancements in ...

We introduce Quantum Molecular Autoencoder (MolQAE), the first quantum autoencoder to leverage the complete molecular structures. MolQAE uniquely maps SMILES strings directly to quantum states using ...

Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, Louisiana 70803, United States Center for Computation and Technology, Louisiana State University, Baton Rouge, ...

Disentangled variational autoencoder (D-VAE) separates materials properties from the latent space by conditioning to make inverse materials design more efficient and transparent. It combines labeled ...

Step 1: Train an AutoEncoder (AE) on pressure maps to obtain meaningful latent embeddings. Step 2: Build a prediction model that maps from other data (e.g., keypoints) to the AE’s latent space. Step 3 ...

The key challenge in the image autoencoding process is to create high-quality reconstructions that can retain fine details, especially when the image data has undergone compression. Traditional ...

This image provides a visual representation of an autoencoder neural network, focusing on the three main components the encoder, hidden layer, and decoder. It depicts the process of data compression ...