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pro vyhledávání: '"Hidden layer"'
On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing
Autor:
Behboodi, Arash, Cesa, Gabriele
Weight sharing, equivariance, and local filters, as in convolutional neural networks, are believed to contribute to the sample efficiency of neural networks. However, it is not clear how each one of these design choices contribute to the generalizati
Externí odkaz:
http://arxiv.org/abs/2411.14288
Autor:
Huang, Changcun
A neural network with one hidden layer or a two-layer network (regardless of the input layer) is the simplest feedforward neural network, whose mechanism may be the basis of more general network architectures. However, even to this type of simple arc
Externí odkaz:
http://arxiv.org/abs/2411.06728
In this work, we consider the problem of learning one hidden layer ReLU neural networks with inputs from $\mathbb{R}^d$. We show that this learning problem is hard under standard cryptographic assumptions even when: (1) the size of the neural network
Externí odkaz:
http://arxiv.org/abs/2410.03477
Convolutional neural networks (CNNs), one of the key architectures of deep learning models, have achieved superior performance on many machine learning tasks such as image classification, video recognition, and power systems. Despite their success, C
Externí odkaz:
http://arxiv.org/abs/2407.11031
Autor:
Hahm, N. W.1 nhahm@inu.ac.kr
Publikováno v:
Journal of Analysis & Applications. Sep2024, Vol. 22 Issue 2, p69-79. 11p.
We present the hidden-layer concatenated physics informed neural network (HLConcPINN) method, which combines hidden-layer concatenated feed-forward neural networks, a modified block time marching strategy, and a physics informed approach for approxim
Externí odkaz:
http://arxiv.org/abs/2406.06350
Akademický článek
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Akademický článek
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Autor:
Stojnic, Mihailo
Recent progress in studying \emph{treelike committee machines} (TCM) neural networks (NN) in \cite{Stojnictcmspnncaprdt23,Stojnictcmspnncapliftedrdt23,Stojnictcmspnncapdiffactrdt23} showed that the Random Duality Theory (RDT) and its a \emph{partiall
Externí odkaz:
http://arxiv.org/abs/2402.05719
We perform accurate numerical experiments with fully-connected (FC) one-hidden layer neural networks trained with a discretized Langevin dynamics on the MNIST and CIFAR10 datasets. Our goal is to empirically determine the regimes of validity of a rec
Externí odkaz:
http://arxiv.org/abs/2401.11004