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Several recent works have shown that state-of-the-art classifiers are vulnerable to worst-case (i.e., adversarial) perturbations of the datapoints. On the other hand, it has been empirically observed that these same classifiers are relatively robust to ran ...
The minimization of empirical risks over finite sample sizes is an important problem in large-scale machine learning. A variety of algorithms has been proposed in the literature to alleviate the computational burden per iteration at the expense of converge ...
In this paper, we propose a method for modeling trajectory patterns with both regional and velocity observations through the probabilistic topic model. By embedding Gaussian models into the discrete topic model framework, our method uses continuous velocit ...
Proper initialization is one of the most important prerequisites for fast convergence of feed-forward neural networks like high order and multilayer perceptrons. This publication aims at determining the optimal variance (or range) for the initial weights a ...
In order to assist the field of neural networks in its maturing, a formalization and a solid foundation are essential. Additionally, to permit the introduction of formal proofs, it is essential to have an all encompassing formal mathematical definition of ...
This report presents one month trainee work on development of French Automatic Speech Recognition ASR system using a french part of multilingual database GlobalPhone_FR. The purpose of this report is to explain and give results of the training and testing ...
Process Analytical Chemistry/Technology has tremendously evolved in the last decades due to the development of multivariate online sensors that are able to monitor the properties of industrial processes in real time [1, 2]. Online monitoring of product qua ...
In this paper, we propose a scalable algorithm for spectral embedding. The latter is a standard tool for graph clustering. However, its computational bottleneck is the eigendecomposition of the graph Laplacian matrix, which prevents its application to larg ...