Results 281 to 290 of about 8,736,056 (314)
Some of the next articles are maybe not open access.
International Journal of Semantic Computing, 2016
Deep learning is a branch of machine learning that tries to model high-level abstractions of data using multiple layers of neurons consisting of complex structures or non-liner transformations. With the increase of the amount of data and the power of computation, neural networks with more complex structures have attracted widespread attention and been
Xing Hao, Guigang Zhang, Shang Ma
openaire +3 more sources
Deep learning is a branch of machine learning that tries to model high-level abstractions of data using multiple layers of neurons consisting of complex structures or non-liner transformations. With the increase of the amount of data and the power of computation, neural networks with more complex structures have attracted widespread attention and been
Xing Hao, Guigang Zhang, Shang Ma
openaire +3 more sources
Intelligent Service Robotics, 2021
This article is about deep learning (DL) and deep reinforcement learning (DRL) works applied to robotics. Both tools have been shown to be successful in delivering data-driven solutions for robotics tasks, as well as providing a natural way to develop an end-to-end pipeline from the robot’s sensing to its actuation, passing through the generation of a ...
Eduardo F. Morales 0001 +3 more
openaire +1 more source
This article is about deep learning (DL) and deep reinforcement learning (DRL) works applied to robotics. Both tools have been shown to be successful in delivering data-driven solutions for robotics tasks, as well as providing a natural way to develop an end-to-end pipeline from the robot’s sensing to its actuation, passing through the generation of a ...
Eduardo F. Morales 0001 +3 more
openaire +1 more source
Encyclopedia with Semantic Computing and Robotic Intelligence, 2017
Artificial intelligence is one of the most beautiful dreams of mankind. Although computer technology has made considerable progress, so far, there is no computer showing intelligence like human beings. The emergence of deep learning gives people a glimmer of hope. So, what is learning deep? Why is it so important? How does it work?
Xing Hao, Guigang Zhang
openaire +2 more sources
Artificial intelligence is one of the most beautiful dreams of mankind. Although computer technology has made considerable progress, so far, there is no computer showing intelligence like human beings. The emergence of deep learning gives people a glimmer of hope. So, what is learning deep? Why is it so important? How does it work?
Xing Hao, Guigang Zhang
openaire +2 more sources
2019
The chapter is devoted at illustrating the basic principles and the current results which characterize the research on Deep Learning. The term refers to the theory and practice of devising and training complex neural networks for supervised and unsupervised tasks.
Massimo Guarascio +2 more
openaire +2 more sources
The chapter is devoted at illustrating the basic principles and the current results which characterize the research on Deep Learning. The term refers to the theory and practice of devising and training complex neural networks for supervised and unsupervised tasks.
Massimo Guarascio +2 more
openaire +2 more sources
Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014
Building intelligent systems that are capable of extracting high-level representations from high-dimensional data lies at the core of solving many AI related tasks, including visual object or pattern recognition, speech perception, and language understanding. Theoretical and biological arguments strongly suggest that building such systems requires deep
openaire +2 more sources
Building intelligent systems that are capable of extracting high-level representations from high-dimensional data lies at the core of solving many AI related tasks, including visual object or pattern recognition, speech perception, and language understanding. Theoretical and biological arguments strongly suggest that building such systems requires deep
openaire +2 more sources
Deep Questioning and Deep Learning
Academic Radiology, 2012T here is relatively strong empirical evidence that an effective means of enhancing learner performance is to pose ‘‘deep questions’’ (1). Examples of deep questioning include asking learners to explore the causes of a sequence of events, the motivations of the people involved, or the quality of the evidence behind a particular practice or theory.
openaire +2 more sources
Learning Deep and Wide: A Spectral Method for Learning Deep Networks
IEEE Transactions on Neural Networks and Learning Systems, 2014Building intelligent systems that are capable of extracting high-level representations from high-dimensional sensory data lies at the core of solving many computer vision-related tasks. We propose the multispectral neural networks (MSNN) to learn features from multicolumn deep neural networks and embed the penultimate hierarchical discriminative ...
Ling Shao 0001 +2 more
openaire +4 more sources
Proceedings of the 24th International Conference on World Wide Web, 2015
Deep learning is a machine learning technology that automatically extracts higher-level representations from raw data by stacking multiple layers of neuron-like units. The stacking allows for extracting representations of increasingly-complex features without time-consuming, offline feature engineering. Recent success of deep learning has shown that it
Kyomin Jung +2 more
openaire +2 more sources
Deep learning is a machine learning technology that automatically extracts higher-level representations from raw data by stacking multiple layers of neuron-like units. The stacking allows for extracting representations of increasingly-complex features without time-consuming, offline feature engineering. Recent success of deep learning has shown that it
Kyomin Jung +2 more
openaire +2 more sources
Nature Computational Science, 2021
The computational complexity of deep neural networks is a major obstacle of many application scenarios driven by low-power devices, including federated learning. A recent finding shows that random sketches can substantially reduce the model complexity without affecting prediction accuracy.
openaire +2 more sources
The computational complexity of deep neural networks is a major obstacle of many application scenarios driven by low-power devices, including federated learning. A recent finding shows that random sketches can substantially reduce the model complexity without affecting prediction accuracy.
openaire +2 more sources