Add to ORKGIn recent years the Deep Neural Networks (DNN) has been using widely in a big range of machine learning and data-mining purposes. This pattern recognition approach can handle highly nonlinear problems. In this work, three main contributions to DNN are presented. 1- A method called Semi Parallel Deep Neural Networks (SPDNN) is introduced wherein several deep architectures are mixed and merged using graph contraction technique to take advantage of all the parent networks. 2- The importance of data is investigated in several attempts and an augmentation technique know as Smart Augmentation is presented. 3- To extract more information from a database, multiple works on Generative Adversarial Networks (GAN) are given wherein the joint d...
Deep learning is an emerging area of machine learning (ML) research. It comprises multiple hidden la...
In recent years, deep artificial neural networks (including recurrent ones) have won numerous con-te...
Generative Adversarial Networks (GANs) have proven to be efficient systems for data generation and o...
In recent years the Deep Neural Networks (DNN) has been using widely in a big range of machine learn...
Deep neural networks (DNNs) are typically trained on specific datasets, optimized with particular di...
Over the last decade the deep neural networks are the revolutionary technique in the domain of arti...
This paper provides a comprehensive study of the latest trends and techniques in deep learning, a ra...
International audienceIn recent years, deep neural networks (DNNs) have known an important rise in p...
In the previous tutorial, I discussed the use of deep networks to classify nonlinear data. In additi...
Learning machines for pattern recognition, such as neural networks or support vector machines, are u...
Deep Neural Networks ("deep learning") have become a ubiquitous choice of algorithms for Machine Lea...
Deep learning has become a pervasive tool in the field of machine learning, delivering unprecedented...
Deep learning is an emerging area of machine learning (ML). It comprises multiple hidden layers of a...
Stacking-based deep neural network (S-DNN) is aggregated with pluralities of basic learning modules,...
Full arxiv preprint version available here: https://arxiv.org/abs/2001.06178A robust theoretical fra...
Deep learning is an emerging area of machine learning (ML) research. It comprises multiple hidden la...
In recent years, deep artificial neural networks (including recurrent ones) have won numerous con-te...
Generative Adversarial Networks (GANs) have proven to be efficient systems for data generation and o...
In recent years the Deep Neural Networks (DNN) has been using widely in a big range of machine learn...
Deep neural networks (DNNs) are typically trained on specific datasets, optimized with particular di...
Over the last decade the deep neural networks are the revolutionary technique in the domain of arti...
This paper provides a comprehensive study of the latest trends and techniques in deep learning, a ra...
International audienceIn recent years, deep neural networks (DNNs) have known an important rise in p...
In the previous tutorial, I discussed the use of deep networks to classify nonlinear data. In additi...
Learning machines for pattern recognition, such as neural networks or support vector machines, are u...
Deep Neural Networks ("deep learning") have become a ubiquitous choice of algorithms for Machine Lea...
Deep learning has become a pervasive tool in the field of machine learning, delivering unprecedented...
Deep learning is an emerging area of machine learning (ML). It comprises multiple hidden layers of a...
Stacking-based deep neural network (S-DNN) is aggregated with pluralities of basic learning modules,...
Full arxiv preprint version available here: https://arxiv.org/abs/2001.06178A robust theoretical fra...
Deep learning is an emerging area of machine learning (ML) research. It comprises multiple hidden la...
In recent years, deep artificial neural networks (including recurrent ones) have won numerous con-te...
Generative Adversarial Networks (GANs) have proven to be efficient systems for data generation and o...