Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper.
Query: Universal Function Approximation by Deep Neural Nets with Bounded Width and ReLU Activations
Error bounds for approximations with deep ReLU networks
A Single Hidden Layer Feedforward Network with Only One Neuron in the Hidden Layer Can Approximate Any Univariate Function
Diverse activation functions in deep learning
Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks
Multilayer feedforward networks with a nonpolynomial activation function can approximate any function
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Comparison of non-linear activation functions for deep neural networks on MNIST classification task
Multi-Bias Non-linear Activation in Deep Neural Networks
Convergence Analysis of Two-layer Neural Networks with ReLU Activation
Opening the neural network black box: an algorithm for extracting rules from function approximating artificial neural networks
Deep and Shallow Architecture of Multilayer Neural Networks
Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks
Sharp Minima Can Generalize For Deep Nets
Deep vs. shallow networks : An approximation theory perspective
A Spline Theory of Deep Networks.
Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions
Shallow Learning For Deep Networks
When and Why Are Deep Networks Better Than Shallow Ones?
Polynomial Bounds for VC Dimension of Sigmoidal and General Pfaffian Neural Networks
Neuroanatomical Evidence in Support of the Bilingual Advantage Theory
Confounding in studies of adverse reactions to vaccines.
Automatic lesion segmentation for melanoma diagnostics in macroscopic images
There exists a neural network that does not make avoidable mistakes
Summarization evaluation using relative utility