Robert Boschgmbh

Carnegie Mellon University

Eric Wong

Jeremy Zico Kolter

Jan Hendrik Metzen

Jeremy Zieg Kolter

Frank R Schmidt

Zico Kolter

Frank Schmidt

Jan Hendrick Metzen

Pratyush Maini

A method for training an automated learning system includes processing training input with a first neural network and processing the output of the first neural network with a second neural network. The input layer of the second neural network corresponding to the output layer of the first neural network. The output layer of the second neural network corresponding to the input layer of the first neural network. An objective function is determined using the output of the second neural network and a predetermined modification magnitude. The objective function is approximated using random Cauchy projections which are propagated through the second neural network.

Method, apparatus and computer program for generating robust automatic learning systems and testing trained automatic learning systems

A system for training a classification model to be robust against perturbations of multiple perturbation types. A perturbation type defines a set of allowed perturbations. The classification model is trained by, in an outer iteration, selecting a set of training instances of a training dataset; selecting, among perturbations allowed by the multiple perturbation types, one or more perturbations for perturbing the selected training instances to maximize a loss function; and updating the set of parameters of the classification model to decrease the loss for the perturbed instances. A perturbation is determined by, in an inner iteration, determining updated perturbations allowed by respective perturbation types of the multiple perturbation types and selecting an updated perturbation that most increases the loss of the classification model.

Classification robust against multiple perturbation types

In a method for training a first neural network a superposed classification is back-propagated through a second neural network. An output value of the second neural network is utilized to determine whether the input of the first neural network is adversarial.

Method, apparatus and computer program for generating robust automated learning systems and testing trained automated learning systems

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Lexington, MA, United States of America

Eric Wong