The Handbook of Brain Theory and Neural Networks

By Hervé Bourlard, Samy Bengio. 2002

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A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines (2018, background): “dern deep learning [9] methods. Under the umbrella of deep learning, multiple techniques have emerged and evolved. These include DBNs (Deep Belief Networks) [10], CNNs (Convolutional Neural Networks) [11], RNNs (Recurrent Neural Networks) [12] as well as LSTMs (Long Short-Term Memory) [13] or AEs (a”
The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation (2018, methodology): “Following RNMT, convolutional neural network based approaches (LeCun and Bengio, 1998) to NMT have recently drawn research attention due to their ability to fully parallelize training to”
Robot Learning Manipulation Action Plans by "Watching" Unconstrained Videos from the World Wide Web (2015, unknown): “Please refer to (LeCun and Bengio 1998) for details.”
DeepReID: Deep Filter Pairing Neural Network for Person Re-identification (2014, background): “Deep learning has achieved great success in solving many computer vision problems, including hand-written digit recognition [22], object recognition [17, 36], object detection [38, 31, 46, 28], image classification [23, 21], scene understanding [8], and face recognition [5, 39, 52, 40].”
Deep architectures for modulation recognition (2017, background): “volutional layers. A convolutional layer consists of N f convolutional ﬁlters. The use of convolutional layers started for image and hand-writing recognition to provide feature translation invariance [8]. The use of convolutional ﬁlters in neural networks may be slightly different than expected for ”
Multi-Speaker DOA Estimation Using Deep Convolutional Networks Trained With Noise Signals (2018, background): “Ns are a variant of the standard fully-connected neural network, where the architecture typically consists of one or more convolution layers followed by fully-connected networks leading ot the output [23]. In this work, the main motivation behind using CNNs is to learn the discriminative features fo”
Deep Learning in Mining Biological Data (2020, background): “locally sensitive, orientation selective neurons in the visual system [41].”
Learning from real users: rating dialogue success with neural networks for reinforcement learning in spoken dialogue systems (2015, methodology): “put layer to make a single prediction of the whole dialogue as depicted in the top half of Figure 2. 2.3. Convolutional neural network model Also investigated was a convolutional neural network (CNN) [25] which has been successfully used for image classiﬁcation [26] and on sequential modelling probl”