Neural Networks and Statistical Learning

By Ke-Lin Du, M. Swamy. 2013

357 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.

Data Driven Governing Equations Approximation Using Deep Neural Networks (2018, background): “Here we cite only a few relatively more recent review/summary type publications [30, 4, 16, 32, 14, 20, 42].”
Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions (2021, methodology): “Multilayer perceptron networks trained by “Backpropagation” type algorithms, self-organizing maps, and radial basis function networks were such innovative methods [27] [37] [36].”
Transformer models for text-based emotion detection: a review of BERT-based approaches (2021, background): “The Recurrent Neural Network (RNN) is a sequential model capable of handling sequential data (Du and Swamy 2013) but limited by the slow training time and the long-range sequence dependencies.”