Kyle Michael Matoba

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https://people.epfl.ch/kyle.matoba

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Related publications (3)

Please note that this is not a complete list of this person’s publications. It includes only semantically relevant works. For a full list, please refer to Infoscience.

Related units (1)

Machine Learning and Optimization Laboratory

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Related publications (3)

Please note that this is not a complete list of this person’s publications. It includes only semantically relevant works. For a full list, please refer to Infoscience.

Safe Deep Neural Networks

Kyle Michael Matoba

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

EPFL2024

Benefits of Max Pooling in Neural Networks: Theoretical and Experimental Evidence

François Fleuret, Kyle Michael Matoba, Nikolaos Dimitriadis

When deep neural networks became state of the art image classifiers, numerous max pooling operations were an important component of the architecture. However, modern computer vision networks typically have few, if any, max pooling operations. To understand ...

2023

Exact Preimages of Neural Network Aircraft Collision Avoidance Systems

François Fleuret, Kyle Michael Matoba

A common pattern of progress in engineering has seen deep neural networks displacing human-designed logic. There are many advantages to this approach, divorcing decisionmaking from human oversight and intuition has costs as well. One is that deep neural ne ...

2020