AI Signal 505
Any network trainable by any algorithm can be extended to reproduce its weights via gradient descent
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Illustration only Photo by Christopher Burns on Unsplash
A paper proves that any neural network trainable by any algorithm can be extended so that gradient descent alone reproduces the same weights and forward output.
This theoretical result shows gradient descent is not inherently limited by architecture, but the construction is not practical. It may inform meta-learning and network design rather than direct engineering practice.
Written by elseif from the cluster below · every claim links back to a sourceThe three things worth knowing
The paper proves a universality result for gradient descent training.
The extension reproduces the weights and forward output of the original network.
The construction is not intended for practical computations.
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