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 ...

A human infant is born with roughly twice as many synapses as it will eventually need. Over the first few years of life, the ...

Can AI learn by shrinking? A new study introduces a development-inspired continual learning framework for spiking neural ...

How does artificial intelligence continue to improve its capabilities? For a long time, expanding model size has been regarded as an important way to ...

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are the ...

Can living neurons replace AI? A new study shows that biological neural networks (BNNs) can be trained to perform reservoir ...

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...

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 ...

The increasing complexity of modern chemical engineering processes presents significant challenges for timely and accurate anomaly detection. Traditional ...

Over the past decades, computer scientists have introduced numerous artificial intelligence (AI) systems designed to emulate the organization and functioning of networks of neurons in the brain.