Postdoctoral researcher Bo Zhao at Harvard University uses mathematical symmetry to decode neural network parameters, ...

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Goal self-hacking AI, a newly coined failure mode in AI safety research, was formally proven unavoidable in fixed-weight ...

Central pattern generators (CPGs) are neural networks that intrinsically produce rhythmic activity, driving essential motor functions like breathing, swallowing and locomotion. These networks are ...

Researchers in Vienna have redesigned dissipative quantum neural networks so each building block can implement any quantum ...

Researchers at KTH Royal Institute of Technology show that geometry-aware normalization lets simple neural networks rapidly approximate Lambert transfer solutions for early-stage interplanetary ...

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We study the effect of normalization on the layers of deep neural networks. A given layer $i$ with $N_{i}$ hidden units is normalized by $1/N_{i}^{\gamma_{i}}$ with ...

The simplified approach makes it easier to see how neural networks produce the outputs they do. A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher.

The strength of certain neural connections can predict how well someone can learn math, and mildly electrically stimulating these networks can boost learning, according to a study published in the ...

Machine learning and neural networks are two common terms in AI -- but what do they mean, and how do they differ? What exactly is machine learning? Machine learning is a subset of AI. ML uses an ...