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In computer simulations of possible universes, researchers have discovered that a neural network can infer the amount of matter in a whole universe by studying just one of its galaxies.
Results from neural networks support the idea that brains are “prediction machines” — and that they work that way to conserve energy.
The computational biologist Anne Carpenter creates software that brings the power of machine learning to researchers seeking answers in mountains of cell images.
A new model of learning centers on bursts of neural activity that act as teaching signals — approximating backpropagation, the algorithm behind learning in AI.
To help them explain the shocking success of deep neural networks, researchers are turning to older but better-understood models of machine learning.
Researchers are turning to the mathematics of higher-order interactions to better model the complex connections within their data.
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