Instruct: Given a paper title, retrieve paper titles that are cited by the given paper.
Query: Deep Belief Networks Are Compact Universal Approximators
Deep, Narrow Sigmoid Belief Networks Are Universal Approximators
Why Deep Neural Networks for Function Approximation?
Deep Belief Networks
Approximation capabilities of multilayer feedforward networks
Optimal Approximation with Sparsely Connected Deep Neural Networks
On the quantitative analysis of deep belief networks
A Fast Learning Algorithm for Deep Belief Nets
Stronger generalization bounds for deep nets via a compression approach
The Emergence of Spectral Universality in Deep Networks
Universal Value Function Approximators
Some new results on neural network approximation
A Single Hidden Layer Feedforward Network with Only One Neuron in the Hidden Layer Can Approximate Any Univariate Function
Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks
Multilayer feedforward networks with a nonpolynomial activation function can approximate any function
Low-Rank Approximations for Conditional Feedforward Computation in Deep Neural Networks
Approximation theory and feedforward networks
A Spline Theory of Deep Networks.
Approximation theory of the MLP model in neural networks
Bounds on the Approximation Power of Feedforward Neural Networks
Approximation and Radial-Basis-Function Networks
Best practices for convolutional neural networks applied to visual document analysis
Detecting Risks in the Banking System by Sentiment Analysis
A comparative study of the classification techniques in isolated Mandarin syllable tone recognition
Kernelized structural SVM learning for supervised object segmentation
CRMA: collision-resistant multiple access