1,016 research outputs found
Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality: a Review
The paper characterizes classes of functions for which deep learning can be
exponentially better than shallow learning. Deep convolutional networks are a
special case of these conditions, though weight sharing is not the main reason
for their exponential advantage
Nonparametric regression using deep neural networks with ReLU activation function
Consider the multivariate nonparametric regression model. It is shown that
estimators based on sparsely connected deep neural networks with ReLU
activation function and properly chosen network architecture achieve the
minimax rates of convergence (up to -factors) under a general
composition assumption on the regression function. The framework includes many
well-studied structural constraints such as (generalized) additive models.
While there is a lot of flexibility in the network architecture, the tuning
parameter is the sparsity of the network. Specifically, we consider large
networks with number of potential network parameters exceeding the sample size.
The analysis gives some insights into why multilayer feedforward neural
networks perform well in practice. Interestingly, for ReLU activation function
the depth (number of layers) of the neural network architectures plays an
important role and our theory suggests that for nonparametric regression,
scaling the network depth with the sample size is natural. It is also shown
that under the composition assumption wavelet estimators can only achieve
suboptimal rates.Comment: article, rejoinder and supplementary materia
Approximation of Nonlinear Functionals Using Deep ReLU Networks
In recent years, functional neural networks have been proposed and studied in
order to approximate nonlinear continuous functionals defined on for integers and . However, their theoretical
properties are largely unknown beyond universality of approximation or the
existing analysis does not apply to the rectified linear unit (ReLU) activation
function. To fill in this void, we investigate here the approximation power of
functional deep neural networks associated with the ReLU activation function by
constructing a continuous piecewise linear interpolation under a simple
triangulation. In addition, we establish rates of approximation of the proposed
functional deep ReLU networks under mild regularity conditions. Finally, our
study may also shed some light on the understanding of functional data learning
algorithms
When Deep Learning Meets Polyhedral Theory: A Survey
In the past decade, deep learning became the prevalent methodology for
predictive modeling thanks to the remarkable accuracy of deep neural networks
in tasks such as computer vision and natural language processing. Meanwhile,
the structure of neural networks converged back to simpler representations
based on piecewise constant and piecewise linear functions such as the
Rectified Linear Unit (ReLU), which became the most commonly used type of
activation function in neural networks. That made certain types of network
structure \unicode{x2014}such as the typical fully-connected feedforward
neural network\unicode{x2014} amenable to analysis through polyhedral theory
and to the application of methodologies such as Linear Programming (LP) and
Mixed-Integer Linear Programming (MILP) for a variety of purposes. In this
paper, we survey the main topics emerging from this fast-paced area of work,
which bring a fresh perspective to understanding neural networks in more detail
as well as to applying linear optimization techniques to train, verify, and
reduce the size of such networks
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