Pages that link to "Item:Q1603592"
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The following pages link to Hardness results for neural network approximation problems (Q1603592):
Displaying 17 items.
- Rank correlation estimators and their limiting distributions (Q451344) (← links)
- On the performance guarantee of neural networks for NP-hard optimization problems (Q672967) (← links)
- Boolean autoencoders and hypercube clustering complexity (Q690669) (← links)
- Finding approximate solutions to NP-hard problems by neural networks is hard (Q1186583) (← links)
- Some connections between learning and optimization (Q1885804) (← links)
- Topological properties of the set of functions generated by neural networks of fixed size (Q2031060) (← links)
- Best \(k\)-layer neural network approximations (Q2117342) (← links)
- Approximation by neural networks and learning theory (Q2489152) (← links)
- On approximate learning by multi-layered feedforward circuits (Q2581366) (← links)
- Complexity of training ReLU neural network (Q2673242) (← links)
- Theory of Classification: a Survey of Some Recent Advances (Q3373749) (← links)
- Training a Single Sigmoidal Neuron Is Hard (Q4409384) (← links)
- (Q5053289) (← links)
- Loading Deep Networks Is Hard: The Pyramidal Case (Q5696500) (← links)
- The committee machine: computational to statistical gaps in learning a two-layers neural network (Q5854128) (← links)
- Deep learning: a statistical viewpoint (Q5887827) (← links)
- Convolution hierarchical deep-learning neural networks (C-HiDeNN): finite elements, isogeometric analysis, tensor decomposition, and beyond (Q6164266) (← links)
