Pages that link to "Item:Q2181058"
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The following pages link to Probabilistic lower bounds for approximation by shallow perceptron networks (Q2181058):
Displaying 15 items.
- Bounds on the number of units for computing arbitrary dichotomies by multilayer perceptrons (Q1319352) (← links)
- Correlations of random classifiers on large data sets (Q2100403) (← links)
- Uniform approximation rates and metric entropy of shallow neural networks (Q2157931) (← links)
- Universal approximation with quadratic deep networks (Q2185719) (← links)
- Correction of AI systems by linear discriminants: probabilistic foundations (Q2200569) (← links)
- Negative results for approximation using single layer and multilayer feedforward neural networks (Q2226355) (← links)
- Limitations of shallow nets approximation (Q2292226) (← links)
- A lower bound on the competitivity of memoryless algorithms for a generalization of the CNN problem (Q2503284) (← links)
- A Bound on the Precision Required to Estimate a Boolean Perceptron from Its Average Satisfying Assignment (Q3440259) (← links)
- Minimization of Error Functionals over Perceptron Networks (Q3539962) (← links)
- Blessing of dimensionality: mathematical foundations of the statistical physics of data (Q5154201) (← links)
- On Functions Computed on Trees (Q5214392) (← links)
- Universal Approximation Depth and Errors of Narrow Belief Networks with Discrete Units (Q5383782) (← links)
- Lower bounds for artificial neural network approximations: a proof that shallow neural networks fail to overcome the curse of dimensionality (Q6155895) (← links)
- Approximation of classifiers by deep perceptron networks (Q6488832) (← links)
