Neural Networks and Learning Machines

By Simon S. Haykin. 2010

5,464 citations recorded by the source corpus.

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Citations in the corpus

Bayesian Neural Architecture Search using A Training-Free Performance Metric (2020, background): “With the advent of deep learning, deep neural networks DNNs) have gained popularity, and they have been applied to wide variety of problems [1,2].”
Neural Networks for Financial Market Risk Classification (2017, background): “In addition, convolutional networks recognize two-dimensional shapes with a high degree of invariance to translation, scaling, and other forms of distortion [5].”
A data-driven approach to predict the success of bank telemarketing (2014, background): “The multilayer perceptron is the most popular NN architecture [14].”
Byzantine-Tolerant Machine Learning (2017, methodology): “The parameter server updates the parameter vector using the following SGD equation In this paper, we assume that the correct (non-Byzantine) workers compute unbiased estimates of the gradient ∇ Q ( x t ) .”
Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification (2018, methodology): “In this work, we propose a systematic approach for constructing surrogate models using deep neural networks (DNNs) [37, 38, 39, 40].”
An approach to reachability analysis for feed-forward ReLU neural networks (2017, methodology): “To begin this investigation, we consider feed-forward neural-networks (FFNNs) only, possibly with several layers, where the activation function is governed by ReLU functions [9, 24].”
A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification (2011, background): “…to read the final version published in 2011 in the IEEE Jour- nal of Selected Topics in Signal Processing (IEEE JSTSP), please go to: 10.1109/JSTSP.2011.2139193 Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines.”
Data Classification: Algorithms and Applications (2014, background): “Neural networks are described in detail in [19, 51].”
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2019, methodology): “design and deployment. In this section, we compendiously introduce DL and related technical terms, paving the way for discussing the integration of DL and edge computing (more details can be found in [79]). TO BE APPEARED IN IEEE COMMUNICATIONS SURVEYS & TUTORIALS 8 A. Neural Networks in Deep Le”
Towards SMS Spam Filtering: Results under a New Dataset (2013, methodology): “Multilayer Perceptron [33] Linear Support Vector Machine – SVM [34], [3] Sequential Minimal Optimization – SMO [35]”
Using sensitivity analysis and visualization techniques to open black box data mining models (2013, background): “Interest in this field arose due to the advances of Information Technology and rapid growth of business and scientific databases [15].”