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Ph.D de

Ph.D
Group : Learning and Optimization

Verification and validation of Machine Learning techniques

Starts on 06/02/2020
Advisor : SCHOENAUER, Marc

Funding : Contrat doctoral uniquement recherche
Affiliation : Université Paris-Saclay
Laboratory : LRI - AO

Defended on 09/11/2021, committee :
Direction de thèse :
- M. Marc SCHOENAUER

Rapporteurs :
- M. Antoine MINÉ
- M. Pawan KUMAR

Co-encadrants de thèse :
- M. Zakaria CHIHANI
- M. Guillaume CHARPIAT

Examinateurs :
- Mme Sylvie PUTOT
- Mme Caterina URBAN
- M. Gilles DOWEK

Research activities :

Abstract :
Machine Learning techniques, Neural Networks in particular, are going through an impressive expansion, permeating various domains, becoming the next frontier for human societies. Autonomous vehicles, aircraft collision avoidance, cancer detection, justice advisors, or mooring line failure detection are but a few examples of Neural Networks applications.
This effervescence, however, may hold more than benefits, as it slowly but surely reaches critical systems.
Indeed, the remarkable efficiency of neural nets comes at a price, more and more underlined by the scientific consensus: weakness to environmental or adversarial perturbations, unpredictability... which prevents their full-scale integration into critical systems.
While the domain of critical software enjoys a plethora of methods that help verify and validate software (abstract interpretation, model checking, simulation, bounded tests...), these methods are generally useless when it comes to Neural Nets.
This thesis aims at bridging formal software verification and machine learning, in order to bring trust in critical systems incorporating Neural Networks elements.
We first study the exact causes that prevent a straightforward application of existing verification techniques on Neural Nets. We state that those issues are three fold: the lack of formal specification on the inputs, the piecewise linear structure that yield a combinatorial explosion and the lack of a common representation.
To tackle those issues, we present CAMUS, a theoretical framework allowing the specification of verification problems on perceptual inputs using simulators. We exploit the piecewise linear structure of neural networks on DISCO, an algorithm of parallel verification, to circumvent the combinatory explosion. We implement those contributions into ISAIEH, a prototypal platform for neural network encoding and verification.

Ph.D. dissertations & Faculty habilitations
MICRO VISUALIZATIONS: DESIGN AND ANALYSIS OF VISUALIZATIONS FOR SMALL DISPLAY SPACES
The topic of this habilitation is the study of very small data visualizations, micro visualizations, in display contexts that can only dedicate minimal rendering space for data representations. For several years, together with my collaborators, I have been studying human perception, interaction, and analysis with micro visualizations in multiple contexts. In this document I bring together three of my research streams related to micro visualizations: data glyphs, where my joint research focused on studying the perception of small-multiple micro visualizations, word-scale visualizations, where my joint research focused on small visualizations embedded in text-documents, and small mobile data visualizations for smartwatches or fitness trackers. I consider these types of small visualizations together under the umbrella term ``micro visualizations.'' Micro visualizations are useful in multiple visualization contexts and I have been working towards a better understanding of the complexities involved in designing and using micro visualizations. Here, I define the term micro visualization, summarize my own and other past research and design guidelines and outline several design spaces for different types of micro visualizations based on some of the work I was involved in since my PhD.

A NEW GENERATION OF GRAPH NEURAL NETWORKS TO TACKLE AMORPHOUS MATERIALS


SPOTTING NEURAL NETWORK BOTTLENECKS AND FIXING THEM BY ARCHITECTURE GROWTH