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A Neural-Network-Based Convex Regularizer for Inverse Problems

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A Formally Verified NAT

George Candea, Solal Vincenzo Pirelli, Arseniy Zaostrovnykh, Luis David Figueiredo Mascarenhas Moreira Pedrosa

We present a Network Address Translator (NAT) written in C and proven to be semantically correct according to RFC 3022, as well as crash-free and memory-safe. There exists a lot of recent work on network verification, but it mostly assumes models of networ ...

Assoc Computing Machinery2017

Acoustic DoA Estimation by One Unsophisticated Sensor

Martin Vetterli, Ivan Dokmanic, Dalia Salem Hassan Fahmy El Badawy

We show how introducing known scattering can be used in direction of arrival estimation by a single sensor. We first present an analysis of the geometry of the underlying measurement space and show how it enables localizing white sources. Then, we extend t ...

Springer Verlag2017

Application of Artificial Neural Network to Predict Survival Time for Patients with Bladder Cancer

Rafal Tomasz Dlugosz

This paper presents an application of an artificial neural network to determine survival time of patients with a bladder cancer. Different learning methods have been investigated to find a solution, which is most optimal from a computational complexity poi ...

Springer-Verlag2009

Regularization Networks for Inverse Problems: A state-space approach

Giancarlo Ferrari Trecate

The solution of linear inverse problems obtained by means of regularization theory has the structure of a neural network similar to classical RBF networks. However, the basis functions depend in a nontrivial way on the specific linear operator to be invert ...

2000

The Effects of Optical Thresholding in Backpropagation Neural Networks

Sigmoid-like activation functions implemented in analog hardware differ in various ways from the standard sigmoidal function as they are asymmetric, truncated, and have a non-standard gain. It is demonstrated how one can adapt the backpropagation learning ...

EC2 & Cie1995

Adaptive Multilayer Optical Neural Network Design

An adaptive multilayer dual-wavelength optical neural network design with all-optical forward propagation, based on a large number of modifiable optical interconnections and a special weight discretization algorithm to compensate for em noise, is described ...

IDIAP1994

An RBF Network that Learns Some Aspects of Perceptual Organization

This paper describes a simple Radial Basis Function network (RBF network) that captures some of the higher order statistical properties important for certain kinds of perceptual organization. Data is presented that demonstrates that the proposed RBF networ ...

IDIAP1993