Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Chihani, Zakaria"'
Autor:
Xu-Darme, Romain, Benois-Pineau, Jenny, Giot, Romain, Quénot, Georges, Chihani, Zakaria, Rousset, Marie-Christine, Zhukov, Alexey
In the field of Explainable AI, multiples evaluation metrics have been proposed in order to assess the quality of explanation methods w.r.t. a set of desired properties. In this work, we study the articulation between the stability, correctness and p
Externí odkaz:
http://arxiv.org/abs/2311.12860
Autor:
Xu-Darme, Romain, Girard-Satabin, Julien, Hond, Darryl, Incorvaia, Gabriele, Chihani, Zakaria
Publikováno v:
Computer Safety, Reliability, and Security. SAFECOMP 2023 Workshops, Sep 2023, Toulouse, France
In this work, we propose CODE, an extension of existing work from the field of explainable AI that identifies class-specific recurring patterns to build a robust Out-of-Distribution (OoD) detection method for visual classifiers. CODE does not require
Externí odkaz:
http://arxiv.org/abs/2311.12855
Autor:
Xu-Darme, Romain, Girard-Satabin, Julien, Hond, Darryl, Incorvaia, Gabriele, Chihani, Zakaria
Out-of-distribution (OoD) detection for data-based programs is a goal of paramount importance. Common approaches in the literature tend to train detectors requiring inside-of-distribution (in-distribution, or IoD) and OoD validation samples, and/or i
Externí odkaz:
http://arxiv.org/abs/2302.10303
In this work, we perform an in-depth analysis of the visualisation methods implemented in two popular self-explaining models for visual classification based on prototypes - ProtoPNet and ProtoTree. Using two fine-grained datasets (CUB-200-2011 and St
Externí odkaz:
http://arxiv.org/abs/2302.08508
In this paper, we present PARTICUL, a novel algorithm for unsupervised learning of part detectors from datasets used in fine-grained recognition. It exploits the macro-similarities of all images in the training set in order to mine for recurring patt
Externí odkaz:
http://arxiv.org/abs/2206.13304
Autor:
Girard-Satabin, Julien, Alberti, Michele, Bobot, François, Chihani, Zakaria, Lemesle, Augustin
Publikováno v:
AISafety, Jul 2022, Vienne, Austria
We present CAISAR, an open-source platform under active development for the characterization of AI systems' robustness and safety. CAISAR provides a unified entry point for defining verification problems by using WhyML, the mature and expressive lang
Externí odkaz:
http://arxiv.org/abs/2206.03044
Autor:
Girard-Satabin, Julien, Varasse, Aymeric, Schoenauer, Marc, Charpiat, Guillaume, Chihani, Zakaria
The impressive results of modern neural networks partly come from their non linear behaviour. Unfortunately, this property makes it very difficult to apply formal verification tools, even if we restrict ourselves to networks with a piecewise linear s
Externí odkaz:
http://arxiv.org/abs/2105.07776
The topic of provable deep neural network robustness has raised considerable interest in recent years. Most research has focused on adversarial robustness, which studies the robustness of perceptive models in the neighbourhood of particular samples.
Externí odkaz:
http://arxiv.org/abs/1911.10735
The theory of quantifier-free bit-vectors (QF_BV) is of paramount importance in software verification. The standard approach for satisfiability checking reduces the bit-vector problem to a Boolean problem, leveraging the powerful SAT solving techniqu
Externí odkaz:
http://arxiv.org/abs/1706.09229
Autor:
Pedrouzo-Ulloa, Alberto, Ramon, Jan, Duflot, Patrick, Pérez-González, Fernando, Lilova, Siyanna, Chihani, Zakaria, Gentili, Nicola, Ulivi, Paola, Hoque, Mohammad Ashadul, Mukammel, Twaha, Pritzker, Zeev, Lemesle, Augustin, Loureiro-Acuña, Jaime, Martı́nez, Xavier, Jiménez-Balsa, Gonzalo
Publikováno v:
IEEE CSR 2P-DPA workshop-Workshop on Privacy-Preserving Data Processing and Analysis
IEEE CSR 2P-DPA workshop-Workshop on Privacy-Preserving Data Processing and Analysis, Jul 2023, Venice, Italy
IEEE CSR 2P-DPA workshop-Workshop on Privacy-Preserving Data Processing and Analysis, Jul 2023, Venice, Italy
International audience; This paper is an overview of the EU-funded project TRUMPET (https://trumpetproject.eu/), and gives an outline of its scope and main technical aspects and objectives. In recent years, Federated Learning has emerged as a revolut
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0233a49b38a2acd6b180f877da80f535
https://inria.hal.science/hal-04092216/document
https://inria.hal.science/hal-04092216/document