Neural networks are computing systems designed to mimic both the structure and function of the human brain. Caltech ...

Neural networks have emerged as a powerful framework for addressing complex problems across numerous scientific domains. In particular, the interplay between neural network models and constraint ...

Deep neural networks have gained fame for their capability to process visual information. And in the past few years, they have become a key component of many computer vision applications. Among the ...

Recent advances at the intersection of neural networks and inverse scattering problems have transformed traditional approaches to imaging and material characterisation. Inverse scattering involves ...

Mark R. Anderson of Strategic News Service, the Future in Review Conferences and Pattern Computer. Popular methods of artificial intelligence have an “explainability problem” — the inability to see ...

Deep neural networks have gained fame for their capability to process visual information. And in the past few years, they have become a key component of many computer vision applications. Among the ...

The core of quantum network research lies in efficiently and reliably establishing entanglement between nodes; however, the challenges of maintaining fragile quantum states are far more complex than ...

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Deep neural networks will move past their ...

An AI-driven digital-predistortion (DPD) framework can help overcome the challenges of signal distortion and energy ...

As the name suggests, neural networks are inspired by the brain. A neural network is designed to mimic how our brains work to recognize complex patterns and improve over time. Neural networks train ...