One of the biggest problems facing modern microelectronics is that computer chips can no longer be made arbitrarily smaller ...
Deep learning is increasingly used in financial modeling, but its lack of transparency raises risks. Using the well-known ...
Progress will come from systems that can combine language understanding with explicit spatial and structural reasoning.
Integrating deep learning in optical microscopy enhances image analysis, overcoming traditional limitations and improving ...
Across the physical world, many intricate structures form via symmetry breaking. When a system with inherent symmetry ...
For example, a Convolutional Neural Network (CNN) trained on thousands of radar echoes can recognize the unique spatial signature of a small metallic fragment, even when its signal is partially masked ...
A deep learning framework combines convolutional and bidirectional recurrent networks to improve protein function prediction from genomic sequences. By automating feature extraction and capturing long ...
The 2026 International Production & Processing Expo (IPPE) is about to open its doors, and Shanghai Xiashu Intelligent ...
“We must strive for better,” said IBM Research chief scientist Ruchir Puri at a conference on AI acceleration organised by the computer company and the IEEE in November. He expects almost all language ...
The neural network approach uses multiple or “deep” layers that learn to identify increasingly complex features in data. The ...
Intrusion detection systems, long constrained by high false-positive rates and limited adaptability, are being re-engineered ...
WiMi Studies Quantum Hybrid Neural Network Model to Empower Intelligent Image Classification BEIJING, Jan. 15, 2026––WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results