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Deep learning

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
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Deep Learning

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, Giuseppe Manco, Ettore Ritacco   +2 more
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Deep-learning seismology

Science, 2022
Seismic waves from earthquakes and other sources are used to infer the structure and properties of Earth’s interior. The availability of large-scale seismic datasets and the suitability of deep-learning techniques for seismic data processing have pushed deep learning to the forefront of fundamental, long-standing research investigations in seismology ...
S. Mostafa Mousavi, Gregory C. Beroza
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Deep learning

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
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Deep Questioning and Deep Learning

Academic Radiology, 2012
T 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.
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Learning Deep and Wide: A Spectral Method for Learning Deep Networks

IEEE Transactions on Neural Networks and Learning Systems, 2014
Building 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, Di Wu 0009, Xuelong Li 0001   +2 more
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Ontology Learning in the Deep

2016
Recent developments in the area of deep learning have been proved extremely beneficial for several natural language processing tasks, such as sentiment analysis, question answering, and machine translation. In this paper we exploit such advances by tailoring the ontology learning problem as a transductive reasoning task that learns to convert knowledge
Petrucci, Giulio, Ghidini, Chiara, Rospocher, Marco   +2 more
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Deep Reinforcement Learning: From Q-Learning to Deep Q-Learning

2017
As the two hottest branches of machine learning, deep learning and reinforcement learning both play a vital role in the field of artificial intelligence. Combining deep learning with reinforcement learning, deep reinforcement learning is a method of artificial intelligence that is much closer to human learning.
Fuxiao Tan, Pengfei Yan, Xinping Guan
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Deep Learning Interpretation

Proceedings of the 26th ACM international conference on Multimedia, 2018
Deep learning has been successfully exploited in addressing different multimedia problems in recent years. The academic researchers are now transferring their attention from identifying what problem deep learning CAN address to exploring what problem deep learning CAN NOT address. This tutorial starts with a summarization of six 'CAN NOT' problems deep
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Deep learning for compilers.

2020
Constructing compilers is hard. Optimising compilers are multi-million dollar projects spanning years of development, yet remain unable to fully exploit the available performance, and are prone to bugs. The rapid transition to heterogeneous parallelism and diverse architectures has raised demand for aggressively-optimising compilers to an all time high,
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