Deep Learning for NLP (without Magic)

By Richard Socher, Yoshua Bengio, Christopher D. Manning. 2013

180 citations recorded by the source corpus.

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Citations in the corpus

DeepTox: Toxicity Prediction using Deep Learning (2016, background): “, 2013 ; Socher and Manning , 2013 ; Baldi et al .”
Deep Sequential Neural Network (2014, background): “The use of deep architectures have shown impressive results for many different tasks, from image classiﬁcation [10, 14], speech recognition [7] to machine translation [19] or even for natural language processing [16].”
WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis (2019, methodology): “CNN as a kind of DL architecture has been broadly applied to many areas [16], such as natural language processing [17], speech recognition [18], computer vision [19].”
Adversarial Distillation for Efficient Recommendation with External Knowledge (2018, background): “Deep neural network has been demonstrated to be effective in many domains, such as computer vision [21], neural language processing [40], and information retrieval [14].”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “Recent remarkable achievements obtained by deep learning in Natural Language Processing (NLP) and Automatic Speech Recognition (ASR) are also embraced by applications for mobile devices.”
DCNN-Based Multi-Signal Induction Motor Fault Diagnosis (2020, background): “With the improvement of computing abilities and advanced techniques of network training, DL has succeeded in various applications, such as speech recognition, natural language processing [21], [22], computer vision [23], and natural science including physics and bioinformatics [24].”
Is normalization indispensable for training deep neural network? (2020, background): “Deep neural networks have greatly advanced the benchmarks in many artiﬁcial intelligence applications, such as image recognition [19], speech recognition [1], and natural language processing [32], etc.”
State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems (2017, methodology): “The learned weights, via this process, are eventually used to initialize a deep neural network [43].”