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Complexity Results on Learning by Neural Nets

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Outgoing claims: the papers this work cites, builds on, or argues with.

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The Polynomial Time Hierarchy
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Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
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Connectionist learning procedures
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The polynomial-time hierarchy
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Complete sets and the polynomial-time hierarchy
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On the complexity of loading shallow neural networks
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A theory of the learnable
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A Theory of the Learnable
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Learnability and the Vapnik-Chervonenkis dimension
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Word problems requiring exponential time(Preliminary Report)
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What Size Net Gives Valid Generalization?
inferredcites →https://doi.org/10.1162/neco.1989.1.1.39openalex-snapshot
Modular Construction of Time-Delay Neural Networks for Speech Recognition
inferredcites →https://doi.org/10.21236/ada164453openalex-snapshot
Learning Internal Representations by Error Propagation
inferredcites →https://doi.org/10.7551/mitpress/5236.001.0001openalex-snapshot
Parallel Distributed Processing

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Inferences Modeled with Neural Networks
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