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Found 7 papers, 1 papers with code
Constructive universal distribution generation through deep ReLU networks
We present an explicit deep network construction that transforms uniformly distributed one-dimensional noise into an arbitrarily close approximation of any two-dimensional target distribution of finite differential entropy and Lipschitz-continuous pdf.
High-Dimensional Distribution Generation Through Deep Neural Networks
We show that every $d$-dimensional probability distribution of bounded support can be generated through deep ReLU networks out of a $1$-dimensional uniform input distribution.
Constructive Universal High-Dimensional Distribution Generation through Deep ReLU Networks
We present an explicit deep neural network construction that transforms uniformly distributed one-dimensional noise into an arbitrarily close approximation of any two-dimensional Lipschitz-continuous target distribution.
Deep Neural Network Approximation Theory
This paper develops fundamental limits of deep neural network learning by characterizing what is possible if no constraints are imposed on the learning algorithm and on the amount of training data.
The universal approximation power of finite-width deep ReLU networks
We show that finite-width deep ReLU neural networks yield rate-distortion optimal approximation (B\"olcskei et al., 2018) of polynomials, windowed sinusoidal functions, one-dimensional oscillatory textures, and the Weierstrass function, a fractal function which is continuous but nowhere differentiable.
Convolutional Recurrent Neural Networks for Electrocardiogram Classification
We propose two deep neural network architectures for classification of arbitrary-length electrocardiogram (ECG) recordings and evaluate them on the atrial fibrillation (AF) classification data set provided by the PhysioNet/CinC Challenge 2017.
Faster Coordinate Descent via Adaptive Importance Sampling
Coordinate descent methods employ random partial updates of decision variables in order to solve huge-scale convex optimization problems.
