1,217 research outputs found
Approximation paper, part 1
In this paper we discuss approximations between neural nets, fuzzy expert systems, fuzzy controllers, and continuous processes
Deep Nonlinear Non-Gaussian Filtering for Dynamical Systems
Filtering is a general name for inferring the states of a dynamical system
given observations. The most common filtering approach is Gaussian Filtering
(GF) where the distribution of the inferred states is a Gaussian whose mean is
an affine function of the observations. There are two restrictions in this
model: Gaussianity and Affinity. We propose a model to relax both these
assumptions based on recent advances in implicit generative models. Empirical
results show that the proposed method gives a significant advantage over GF and
nonlinear methods based on fixed nonlinear kernels
Demystifying Deep Learning: A Geometric Approach to Iterative Projections
Parametric approaches to Learning, such as deep learning (DL), are highly
popular in nonlinear regression, in spite of their extremely difficult training
with their increasing complexity (e.g. number of layers in DL). In this paper,
we present an alternative semi-parametric framework which foregoes the
ordinarily required feedback, by introducing the novel idea of geometric
regularization. We show that certain deep learning techniques such as residual
network (ResNet) architecture are closely related to our approach. Hence, our
technique can be used to analyze these types of deep learning. Moreover, we
present preliminary results which confirm that our approach can be easily
trained to obtain complex structures.Comment: To be appeared in the ICASSP 2018 proceeding
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