Categories: AI/ML News

Guided learning helps previously ‘untrainable’ neural networks learn effectively

Even networks long considered “untrainable” can learn effectively with a bit of a helping hand. Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have shown that a brief period of alignment between neural networks, a method they call guidance, can dramatically improve the performance of architectures previously thought unsuitable for modern tasks.

Artificial neural networks learn better when they spend time not learning at all

Researchers discuss how mimicking sleep patterns of the human brain in artificial neural networks may help mitigate the threat of catastrophic forgetting in the latter, boosting their utility across a spectrum of research interests.

November 19, 2022

In "AI/ML News"

When to Use MLP, CNN, and RNN Neural Networks

September 2, 2022

In "AI/ML Research"

Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures

his paper considers the Pointer Value Retrieval (PVR) benchmark introduced in [ZRKB21], where a `reasoning' function acts on a string of digits to produce the label. More generally, the paper considers the learning of logical functions with gradient descent (GD) on neural networks. It is first shown that in order…

October 25, 2022

In "FAANG"

AI Generated Robotic Content