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There has been a resurgence of research in the design of deep architecture models and learning algorithms, i.e., methods that rely on the extraction of a multilayer representation of the data. Often referred to as deep learning, this topic of research has been building on and contributing to many different research topics, such as neural networks, graphical models, feature learning, unsupervised learning, optimization, pattern recognition, and signal processing. Deep learning is also motivated and inspired by neuroscience and has had a tremendous impact on various applications such as computer vision, speech recognition, and natural language processing. The clearly multidisciplinary nature of deep learning led to a call for papers for a special issue dedicated to learning deep architectures.
Index Terms:
Special issues and sections,Learning systems,Computer architecture,Data models,Algorithm design and analysis,Signal processing algorithms,Data mining,Neural networks
Citation:
Samy Bengio, Li Deng, Hugo Larochelle, Honglak Lee, Ruslan Salakhutdinov, "Guest Editors' Introduction: Special Section on Learning Deep Architectures," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1795-1797, Aug. 2013, doi:10.1109/TPAMI.2013.118
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