Continual learning in neural networks addresses the challenge of adapting to new information accumulated over time while retaining previously acquired knowledge. A central obstacle to this process is ...

Overview: Master deep learning with these 10 essential books blending math, code, and real-world AI applications for lasting ...

Broad Learning Systems (BLS) have emerged as a promising alternative to conventional deep learning architectures by utilising random feature mapping and incremental learning paradigms that expand ...

During my first semester as a computer science graduate student at Princeton, I took COS 402: Artificial Intelligence. Toward the end of the semester, there was a lecture about neural networks. This ...

A deep learning framework combines convolutional and bidirectional recurrent networks to improve protein function prediction from genomic ...

The TLE-PINN method integrates EPINN and deep learning models through a transfer learning framework, combining strong physical constraints and efficient computational capabilities to accurately ...

An analysis of over 41 million research papers from the past 40 years has revealed that researchers who use artificial ...

This workshop will consider several applications based on machine learning classification and the training of artificial neural networks and deep learning.

For decades, dopamine has been celebrated in neuroscience as the quintessential "reward molecule"—a chemical herald of ...

AI in medical imaging market growth is driven by deep learning advancements, personalized medicine, lack of radiologists, and AI integration in telemedicine. It faces challenges like high costs, data ...