Largely, we care about NN generalization on complications wherever the enter space is constant, frequently R^n. Â The authors argue which the finite-set results are applicable to these issues, due to the fact you can always discretize R^n to get a finite set. Â I don't Feel this captures the styles of perform complexity we care about for NNs.
Based upon that description, it should be straightforward to generalize the Levin sure to neural networks. The key stage could well be to replace the Huffman code by using a turbocode (or almost every other near-Shannon-certain code), at which place the compressibility is largely similar to the log likelihood density, and we might take the Restrict to continuous operate space with none difficulty.
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As for why most features will not likely generalize properly, It really is most likely since there is way more Erroneous responses than correct types for each new datapoint, usually.
Scott's “How does Gradient Descent Interact with Goodhart?†would seem very related listed here. Possibly these results could function a partial remedy to that dilemma, in the perception that SGD isn't going to seem to vary very much from random search (with a Gaussian prior over the weights) for deep neural networks on MNIST.
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It's genuine there are more Improper solutions than 파워볼중계 suitable responses, but (I assert) you can find much more functions that continue the sequence by giving the right respond to than features that keep on the sequence by providing a Incorrect answer.
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In addition, most functions that match a offered set of coaching details will likely not generalise perfectly to new facts.