Q Labs’ Dust algorithm uses only forward passes to train transformers, and Noise speedrun community entries have now achieved ...

Forget the sensationalist media noise about robots rebelling in science fiction. Look straight at the 2024 Nobel Prize in Physics awarded to Geoffrey Hinton—the pioneer who laid the mathematical ...

A neural network predictive control framework that jointly optimizes thermal comfort, daylighting, energy use, and life-cycle ...

Researchers have trained a two-hidden-layer neural network with Levenberg–Marquardt backpropagation to accurately approximate ...

As artificial intelligence becomes more powerful, its ability to handle humanlike tasks is undeniable. Artificial neural ...

Pretraining Transformers Without Backpropagation" is a proposal for a computational algorithm and training framework that enables the pretraining of Transformer models without using backpropagation ...

The latest news and top stories on Geoffrey Hinton, a British-Canadian cognitive psychologist and computer scientist. Widely ...

Recurrent Looped Transformer (RLT) pairs a causal encoder with a recurrent decoder. The encoder processes tokens in parallel under a causal mask and produces representations e_t, from which key-value ...

Quantinuum researchers have developed a new backpropagation algorithm that significantly reduces the memory demands of optimizing quantum circuits. The method achieves a reduction in memory cost by a ...

That cycle repeats thousands of times until the network gets really good at its job. Most people assume that understanding how neural networks learn requires a PhD in mathematics. It doesn’t. The core ...

Every time you train a neural network, backpropagation is doing the heavy lifting. It's the algorithm that answers the most important question in optimization: how should each weight change to reduce ...