Add to ORKGAbstract. Deep neural networks with several layers have during the last years become a highly successful and popular research topic in machine learning due to their excellent performance in many benchmark prob-lems and applications. A key idea in deep learning is to not only learn the nonlinear mapping between the inputs and outputs, but also the un-derlying structure of the data (input) vectors. In this chapter, we first consider problems with training deep networks using backpropagation type algorithms. After this, we consider various structures used in deep learning, including restricted Boltzmann machines, deep belief networks, deep Boltzmann machines, and nonlinear autoencoders. In the later part of this chapter we discuss in more deta...
Recent theoretical advances in the learning of deep artificial neural networks have made it possible...
Deep learning models stand for a new learning paradigm in artificial intelligence (AI) and machine l...
Deep learning (DL) is playing an increasingly important role in our lives. It has already made a hug...
Abstract. Deep neural networks with several layers have during the last years become a highly succes...
Restricted Boltzmann Machines (RBMs) and autoencoders have been used - in several variants - for sim...
Deep neural networks have become increasingly popular under the name of deep learning recently due t...
We explore the training and usage of the Restricted Boltzmann Machine for unsupervised feature extra...
Since the proposal of a fast learning algorithm for deep belief networks in 2006, the deep learning ...
Abstract We show how deep learning methods can be applied in the context of crowdsourcing and unsupe...
Our thesis wants to illustrate recent developments in ANN, and study the topological properties of a...
Nowadays, the most revolutionary area in computer science is deep learning algorithms and models. Th...
This thesis is a compilation of five research contributions whose goal is to do unsupervised and tra...
Deep Learning (DL) has experienced considerable reach and success in the number of various applicati...
Abstract—The main objective of this paper is to provide a stateof- the-art survey on deep learning m...
Training of the neural autoregressive density estimator (NADE) can be viewed as doing one step of pr...
Recent theoretical advances in the learning of deep artificial neural networks have made it possible...
Deep learning models stand for a new learning paradigm in artificial intelligence (AI) and machine l...
Deep learning (DL) is playing an increasingly important role in our lives. It has already made a hug...
Abstract. Deep neural networks with several layers have during the last years become a highly succes...
Restricted Boltzmann Machines (RBMs) and autoencoders have been used - in several variants - for sim...
Deep neural networks have become increasingly popular under the name of deep learning recently due t...
We explore the training and usage of the Restricted Boltzmann Machine for unsupervised feature extra...
Since the proposal of a fast learning algorithm for deep belief networks in 2006, the deep learning ...
Abstract We show how deep learning methods can be applied in the context of crowdsourcing and unsupe...
Our thesis wants to illustrate recent developments in ANN, and study the topological properties of a...
Nowadays, the most revolutionary area in computer science is deep learning algorithms and models. Th...
This thesis is a compilation of five research contributions whose goal is to do unsupervised and tra...
Deep Learning (DL) has experienced considerable reach and success in the number of various applicati...
Abstract—The main objective of this paper is to provide a stateof- the-art survey on deep learning m...
Training of the neural autoregressive density estimator (NADE) can be viewed as doing one step of pr...
Recent theoretical advances in the learning of deep artificial neural networks have made it possible...
Deep learning models stand for a new learning paradigm in artificial intelligence (AI) and machine l...
Deep learning (DL) is playing an increasingly important role in our lives. It has already made a hug...