116,855 research outputs found
Achieve the Minimum Width of Neural Networks for Universal Approximation
The universal approximation property (UAP) of neural networks is fundamental
for deep learning, and it is well known that wide neural networks are universal
approximators of continuous functions within both the norm and the
continuous/uniform norm. However, the exact minimum width, , for the
UAP has not been studied thoroughly. Recently, using a
decoder-memorizer-encoder scheme, \citet{Park2021Minimum} found that for both the -UAP of ReLU networks and the -UAP of
ReLU+STEP networks, where are the input and output dimensions,
respectively. In this paper, we consider neural networks with an arbitrary set
of activation functions. We prove that both -UAP and -UAP for functions
on compact domains share a universal lower bound of the minimal width; that is,
. In particular, the critical width, ,
for -UAP can be achieved by leaky-ReLU networks, provided that the input
or output dimension is larger than one. Our construction is based on the
approximation power of neural ordinary differential equations and the ability
to approximate flow maps by neural networks. The nonmonotone or discontinuous
activation functions case and the one-dimensional case are also discussed
Discrete synaptic events induce global oscillations in balanced neural networks
Neural dynamics is triggered by discrete synaptic inputs of finite amplitude.
However, the neural response is usually obtained within the diffusion
approximation (DA) representing the synaptic inputs as Gaussian noise. We
derive a mean-field formalism encompassing synaptic shot-noise for sparse
balanced networks of spiking neurons. For low (high) external drives (synaptic
strengths) irregular global oscillations emerge via continuous and hysteretic
transitions, correctly predicted by our approach, but not from the DA. These
oscillations display frequencies in biologically relevant bands.Comment: 6 pages, 3 figure
Deep Network Approximation: Achieving Arbitrary Accuracy with Fixed Number of Neurons
This paper develops simple feed-forward neural networks that achieve the
universal approximation property for all continuous functions with a fixed
finite number of neurons. These neural networks are simple because they are
designed with a simple and computable continuous activation function
leveraging a triangular-wave function and the softsign function. We prove that
-activated networks with width and depth can
approximate any continuous function on a -dimensional hypercube within an
arbitrarily small error. Hence, for supervised learning and its related
regression problems, the hypothesis space generated by these networks with a
size not smaller than is dense in the continuous function
space and therefore dense in the Lebesgue spaces
for . Furthermore, classification functions arising from image
and signal classification are in the hypothesis space generated by
-activated networks with width and depth , when there
exist pairwise disjoint bounded closed subsets of such that the
samples of the same class are located in the same subset. Finally, we use
numerical experimentation to show that replacing the ReLU activation function
by ours would improve the experiment results
Simplified neural networks algorithms for function approximation and regression boosting on discrete input spaces
Function approximation capabilities of feedforward Neural Networks have been widely investigated over the past couple of decades. There has been quite a lot of work carried out in order to prove 'Universal Approximation Property' of these Networks. Most of the work in application of Neural Networks for function approximation has concentrated on problems where the input variables are continuous. However, there are many real world examples around us in which input variables constitute only discrete values, or a significant number of these input variables are discrete. Most of the learning algorithms proposed so far do not distinguish between different features of continuous and discrete input spaces and treat them in more or less the same way. Due to this reason, corresponding learning algorithms becomes unnecessarily complex and time consuming, especially when dealing with inputs mainly consisting of discrete variables. More recently, it has been shown that by focusing on special features of discrete input spaces, more simplified and robust algorithms can be developed. The main objective of this work is to address the function approximation capabilities of Artificial Neural Networks. There is particular emphasis on development, implementation, testing and analysis of new learning algorithms for the Simplified Neural Network approximation scheme for functions defined on discrete input spaces. By developing the corresponding learning algorithms, and testing with different benchmarking data sets, it is shown that comparing conventional multilayer neural networks for approximating functions on discrete input spaces, the proposed simplified neural network architecture and algorithms can achieve similar or better approximation accuracy. This is particularly the case when dealing with high dimensional-low sample cases, but with a much simpler architecture and less parameters. In order to investigate wider implications of simplified Neural Networks, their application has been extended to the Regression Boosting frame work. By developing, implementing and testing with empirical data it has been shown that these simplified Neural Network based algorithms also performs well in other Neural Network based ensembles.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution
Generative Adversarial Networks (GAN) have limitations when the goal is to generate sequences of discrete elements. The reason for this is that samples from a distribution on discrete objects such as the multinomial are not differentiable with respect to the distribution parameters. This problem can be avoided by using the Gumbel-softmax distribution, which is a continuous approximation to a multinomial distribution parameterized in terms of the softmax function. In this work, we evaluate the performance of GANs based on recurrent neural networks with Gumbel-softmax output distributions in the task of generating sequences of discrete elements
Function Approximation with Randomly Initialized Neural Networks for Approximate Model Reference Adaptive Control
Classical results in neural network approximation theory show how arbitrary
continuous functions can be approximated by networks with a single hidden
layer, under mild assumptions on the activation function. However, the
classical theory does not give a constructive means to generate the network
parameters that achieve a desired accuracy. Recent results have demonstrated
that for specialized activation functions, such as ReLUs and some classes of
analytic functions, high accuracy can be achieved via linear combinations of
randomly initialized activations. These recent works utilize specialized
integral representations of target functions that depend on the specific
activation functions used. This paper defines mollified integral
representations, which provide a means to form integral representations of
target functions using activations for which no direct integral representation
is currently known. The new construction enables approximation guarantees for
randomly initialized networks for a variety of widely used activation
functions
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