Scientists at Google DeepMind —the company’s artificial intelligence research arm—say they’ve created an A.I. tool that can ...

The proposed Coordinate-Aware Feature Excitation (CAFE) module and Position-Aware Upsampling (Pos-Up) module both adhere to ...

With PFITRE, Brookhaven scientists achieve breakthrough 3D imaging in nanoscale X-ray tomography, combining AI and physics for superior clarity and precision.

Abstract: Self-supervised monocular depth estimation trains by utilizing the structure of the data itself without relying on ground-truth depth labels, gaining widespread attention in fields such as ...

Accurate reservoir inflow forecasting is vital for effective water resource management. Reliable forecasts enable operators to optimize storage and release strategies to meet competing sectoral ...

Neural networks are designed to learn compressed representations of high-dimensional data, and autoencoders (AEs) are a widely-used example of such models. These systems employ an encoder-decoder ...

Abstract: This study presents a deep learning (DL)-based approach to the seismic velocity inversion problem, focusing on both noisy and noiseless training datasets of varying sizes. Our seismic ...

The landscape of vision model pre-training has undergone significant evolution, especially with the rise of Large Language Models (LLMs). Traditionally, vision models operated within fixed, predefined ...