Results 231 to 240 of about 449,358 (260)
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2018
The previously discussed architecture of ANNs is called FC neural networks (FCNNs). The reason is that each neuron in a layer i is connected to all neurons in layers i-1 and i+1. Each connection between two neurons has two parameters: the weight and the bias. Adding more layers and neurons increases the number of parameters.
Mathew Salvaris +2 more
+4 more sources
The previously discussed architecture of ANNs is called FC neural networks (FCNNs). The reason is that each neuron in a layer i is connected to all neurons in layers i-1 and i+1. Each connection between two neurons has two parameters: the weight and the bias. Adding more layers and neurons increases the number of parameters.
Mathew Salvaris +2 more
+4 more sources
2019
In the last few years, convolutional neural networks (CNNs), along with recurrent neural networks (RNNs), have become a basic building block in constructing complex deep learning solutions for various NLP, speech, and time series tasks. LeCun first introduced certain basic parts of the CNN frameworks as a general NN framework to solve various high ...
Uday Kamath, John Liu, James Whitaker
openaire +1 more source
In the last few years, convolutional neural networks (CNNs), along with recurrent neural networks (RNNs), have become a basic building block in constructing complex deep learning solutions for various NLP, speech, and time series tasks. LeCun first introduced certain basic parts of the CNN frameworks as a general NN framework to solve various high ...
Uday Kamath, John Liu, James Whitaker
openaire +1 more source
2017
Artificial neural networks have flourished in recent years in the processing of unstructured data, especially images, text, audio, and speech. Convolutional neural networks (CNNs) work best for such unstructured data. Whenever there is a topology associated with the data, convolutional neural networks do a good job of extracting the important features ...
openaire +1 more source
Artificial neural networks have flourished in recent years in the processing of unstructured data, especially images, text, audio, and speech. Convolutional neural networks (CNNs) work best for such unstructured data. Whenever there is a topology associated with the data, convolutional neural networks do a good job of extracting the important features ...
openaire +1 more source
Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural Networks
ACM Computing Surveys, 2022ClĂudio Santos, Joao P Papa
exaly
11 TOPS photonic convolutional accelerator for optical neural networks
Nature, 2021Xingyuan Xu, Mengxi, Bill Corcoran
exaly
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects
IEEE Transactions on Neural Networks and Learning Systems, 2022Zewen Li, Fan Liu, Shouheng Peng
exaly
1D convolutional neural networks and applications: A survey
Mechanical Systems and Signal Processing, 2021Serkan Kiranyaz, Onur Avci, Turker Ince
exaly
Explainable Convolutional Neural Networks: A Taxonomy, Review, and Future Directions
ACM Computing Surveys, 2023Rami Ibrahim, M Omair Shafiq
exaly