Deep Learning and Its Applications to Signal and Information Processing
333 citations recorded by the source corpus.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
Citations in the corpus
- Deep Learning: Methods and Applications (2014, background): “Part of the review in this chapter is based on recent publications in [68, 161, 412].”
- A tutorial survey of architectures, algorithms, and applications for deep learning (2014, background): “Part of this review is based on the recent publication of [6, 7, 25].”
- Neural information retrieval: at the end of the early years (2017, background): “For general introductions to deep learning, see [7, 54, 71, 105, 172, 210], and Bengio [16].”
- Statistical parametric speech synthesis using deep neural networks (2013, methodology): “Inspired by the human speech production system which is believed to have layered hierarchical structures in transforming the information from the linguistic level to the waveform level [30], this paper applies a deep architecture to solve the speech synthesis problem.”
- Rolling bearing fault diagnosis using an optimization deep belief network (2015, methodology): “The DBN works through a layer-by-layer successive learning process and it has recently become a popular approach in machine learning for its promising advantages such as fast inference and the ability to encode richer and higher order network structures [18, 22, 27].”
- FACTORIZED DEEP NEURAL NETWORKS FOR ADAPTIVE SPEECH RECOGNITION (2012, background): “Our future directions will also integrate the adaptive modeling strategy presented in this paper to new deep architectures beyond DNNs and develop applications beyond speech recognition [14].”
- An Overview of Deep-Structured Learning for Information Processing (2011, methodology): “Higher accuracy than DBN is reported, especially after a ―fine-tuning‖ technique developed in (Yu and Deng, 2011) is exploited.”
- Data-intensive applications, challenges, techniques and technologies: A survey on Big Data (2014, background): “Deep machine learning has become a new research frontier in artificial intelligence [68,21,69,13,200].”
- A survey of machine learning for big data processing (2016, background): “In contrast to most traditional learning techniques, which are considered using shallow-structured learning architectures, deep learning mainly uses supervised and/or unsupervised strategies in deep architectures to automatically learn hierarchical representations [33].”
- Speech-Centric Information Processing: An Optimization-Oriented Approach (2013, methodology): “Finally, in light of the recent advance in deep learning methods that have dramatically cut down the ASR error rate [17], [44], [92], it is highly desirable to extend the end-to-end optimization approach for the SCIP systems presented in this paper based on Gaussian-HMM ASR subsystems to incorporate”
- Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning (2017, background): “…helped to train DNNs for large-scale applications, such as for speech recognition [40, 35], image classification [18, 47, 77], human action recognition [43], video classification [45], forecasting [56, 36, 81, 83], machine translation [17, 2], and civil engineering [26, 1, 46, 24]; see also [89].”
- Deep Learning and Its Applications in Biomedicine (2018, background): “It attempts to model abstraction from large-scale data by employing multi-layered deep neural networks (DNNs), thus making sense of data such as images, sounds, and texts [1].”
- Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease (2018, background): “tectures, DL develops a layered, hierarchical architecture to yield high-level and more effective data representation [19], [27], [28].”