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By Geoffrey E. Hinton, Li Deng, Dong Yu, George E. Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew W. Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N. Sainath and 1 more. 2017

Subfields: Evaluation & Benchmarks

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Citations in the corpus

Scaling description of generalization with number of parameters in deep learning (2019, background): “sful at a very wide range of tasks. In particular, for supervised learning tasks, they have yielded breakthroughs in various contexts, in particular for image classication [1, 2], speech recognition [3], and automatic translation [4]. Yet, a theoretical framework to understand the remarkable success”
Unsupervised Speech Recognition (2021, background): “Such models are often classified into two categories: hybrid (Juang et al., 1986; Young, 1996; Povey, 2005; Hinton et al., 2012; Abdel-Hamid et al., 2012; Bourlard and Morgan, 2012) or end-to-end (Graves et al., 2006; Graves, 2012; Chorowski et al., 2015; Rao et al., 2017).”
Unsupervised Pre-Training of Bidirectional Speech Encoders via Masked Reconstruction (2020, methodology): “Restricted Boltzmann Machines (RBMs) [1] were widely used to pre-train deep neural networks as part of a speech recognizer [2], often on the same transcribed data used for acoustic modeling.”
Towards Better Decoding and Language Model Integration in Sequence to Sequence Models (2016, background): “Deep learning [1] has led to many breakthroughs including speech and image recognition [2, 3, 4, 5, 6, 7].”
End-to-end attention-based large vocabulary speech recognition (2015, background): “Deep neural networks have become a popular replacement for Gaussian Mixture Models for acoustic modeling in stateof-the-art large vocabulary speech recognition systems [4].”
Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness Against Adversarial Attack (2018, background): “Deep Neural Networks (DNNs) have achieved great success in a variety of applications, including but not limited to image classification [23], speech recognition [17], machine translation [5], and autonomous driving [9].”
Robust Physical-World Attacks on Deep Learning Models (2017, background): “Deep Neural Networks (DNNs) are being applied with great success to a variety of areas ranging from speech processing [6] to medical diagnostics [4] and are increasingly being used to control physical objects such as cars [14], UAVs [2], and robots [30].”
NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles (2017, background): “Deep neural networks (NN) are being successful in accomplishing challenging tasks such as image classification [10] and speech recognition [8].”
MagNet: A Two-Pronged Defense against Adversarial Examples (2017, background): “They are used in autonomous control for robots and vehicles [1, 3, 4], financial systems [32], medical treatments [31], information security [12, 29], and human-computer interaction [11, 13].”
Is Deep Learning Safe for Robot Vision? Adversarial Examples Against the iCub Humanoid (2017, background): “Their flexibility to deal with different kinds of input data, along with their learning capacity, have made them a powerful instrument to successfully tackle challenging applications, reporting impressive performance on several tasks in computer vision, speech recognition and human-robot interaction”
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks (2015, background): “However, these same architectures have been shown to be unstable to small, well sought, perturbations of the images.”
On the Binding Problem in Artificial Neural Networks (2020, background): “speech recognition (Fernández et al., 2007; Hinton et al., 2012), and even in some inherently symbolic domains such as language modeling (Devlin et al.”
Residual LSTM: Design of a Deep Recurrent Architecture for Distant Speech Recognition (2017, methodology): “Kaldi [23] is a toolkit for speech recognition that is used to train a context-dependent LDA-MLLT-GMM-HMM system.”