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Ikram Chraibi Kaadoud - Post doctoral Researcher in Explainable AI - IMT Atlantique, Lab-STICC


Conference papers6 documents

  • M Cazes, N Franiatte, A Delmas, J-M André, M Rodier, et al.. Evaluation of the sensitivity of cognitive biases in the design of artificial intelligence. Rencontres des Jeunes Chercheurs en Intelligence Artificielle (RJCIA'21) Plate-Forme Intelligence Artificielle (PFIA'21), Jul 2021, Bordeaux, France. pp.30-37. ⟨hal-03298746⟩
  • Ikram Chraibi Kaadoud, Lina Fahed, Philippe Lenca. Explainable AI: a narrative review at the crossroad of Knowledge Discovery, Knowledge Representation and Representation Learning. Twelfth International Workshop Modelling and Reasoning in Context (MRC) @IJCAI 2021, Aug 2021, Montréal (virtual), Canada. pp.28-40. ⟨hal-03343687⟩
  • Ikram Chraibi Kaadoud, Lina Fahed. Intelligence artificielle explicable : vers des systèmes transparents, acceptables et éthiques. Women TechMakers 2021, Google, Mar 2021, Montréal et Québec (virtuel), Canada. ⟨hal-03184451⟩
  • Ikram Chraibi Kaadoud. L’intelligence artificielle explicable pour une meilleure compréhension des données naturalistes. Journée d'étude interdisciplinaire MIMESIS "Penser le(s) lien(s)", Groupe de recherche sur les enjeux de la communication (GRESEC); Université de Grenoble Alpes, Sep 2021, Grenoble, France. ⟨hal-03391612⟩
  • Ikram Chraibi Kaadoud. eXplainable Artificial intelligence: From machine to humans, how to make them collaborate?. WiDS 2021 : Women in data science Benguerir @ UM6P : Data science between academia and industry, MSDA (Modeling Simulation & Data Analysis), Mohammed VI Polytechnic University, Mar 2021, Ben Guerir (virtuel), Morocco. ⟨hal-03184424⟩
  • Ikram Chraibi Kaadoud, Nicolas P. Rougier, Frédéric Alexandre. Implicit knowledge extraction and structuration from electrical diagrams. The 30th International Conference on Industrial, Engineering, Other Applications of Applied Intelligent Systems, Jun 2017, Arras, France. ⟨hal-01525028⟩

Journal articles3 documents

  • Laura Joy Boulos, Alexandre Mendes, Alexandra Delmas, Ikram Chraibi Kaadoud. An Iterative and Collaborative End-to-End Methodology Applied to Digital Mental Health. Frontiers in Psychiatry, Frontiers, 2021, 12, ⟨10.3389/fpsyt.2021.574440⟩. ⟨hal-03352727⟩
  • Ikram Chraibi Kaadoud, Nicolas P. Rougier, Frédéric Alexandre. Knowledge extraction from the learning of sequences in a long short term memory (LSTM) architecture. Knowledge-Based Systems, Elsevier, 2021, 235, pp.18. ⟨10.1016/j.knosys.2021.107657⟩. ⟨hal-03437920⟩
  • Ikram Chraibi Kaadoud, Thierry Viéville. L’apprentissage profond : une idée à creuser ?. Interstices, INRIA, 2016. ⟨hal-01309315⟩

Poster communications3 documents

  • Ikram Chraibi Kaadoud, Lina Fahed, Philippe Lenca. Explainable AI: a narrative review at the crossroad of Knowledge Discovery, Knowledge Representation and Representation Learning. Twelfth International Workshop Modelling and Reasoning in Context (MRC) @IJCAI 2021, Aug 2021, Montréal, Canada. 2021. ⟨hal-03345286⟩
  • Ikram Chraibi Kaadoud, Nicolas P. Rougier, Frédéric Alexandre. Modeling Implicit Learning : Extracting Implicit Rules from Sequences using LSTM. WiML 2019 - 14th Women in Machine Learning Workshop at NeurIPS 2019, Dec 2019, Vancouver, Canada. ⟨hal-02491042⟩
  • Charlotte Héricé, Ikram Chraibi Kaadoud, Frédéric Alexandre. Modélisation bio-inspirée dans le numérique et la santé. Fête de la Science 2015 - INRIA Bordeaux Sud-Ouest, Oct 2015, Bordeaux, France. ⟨hal-01230066⟩

Other publications1 document

  • Ikram Chraibi Kaadoud, Thierry Viéville. Finalement l’apprentissage profond : est-ce une idée creuse ? : Mais faut-il approfondir la notion de réseaux de neurones ? Mais pourquoi les réseaux de neurones ne marchent pas si bien ? Mais c'est quoi un neurone artificiel en fait ?. 2015, pp.4. ⟨hal-01238525⟩

Preprints, Working Papers, ...1 document

  • Ikram Chraibi Kaadoud, Nicolas P. Rougier, Frédéric Alexandre. Implicit knowledge extraction and structuration from electrical diagrams. 2016. ⟨hal-01525015v2⟩

Reports1 document

  • Frédéric Alexandre, Denis Chiron, Ikram Chraibi Kaadoud, Martine Courbin-Coulaud, Snigdha Dagar, et al.. Neurosmart, une histoire de cerveau et de passionné·e·s de science. [Rapport Technique] RT-0509, Inria. 2020, pp.19. ⟨hal-03013647⟩

Theses1 document

  • Ikram Chraibi Kaadoud. Apprentissage de séquences et extraction de règles de réseaux récurrents : application au traçage de schémas techniques.. Autre [cs.OH]. Université de Bordeaux, 2018. Français. ⟨NNT : 2018BORD0032⟩. ⟨tel-01771685⟩

Videos2 documents

  • Ikram Chraibi Kaadoud, Lina Fahed, Philippe Lenca. Explainable AI: a narrative review at the crossroad of Knowledge Discovery, Knowledge Representation and Representation Learning.. 2021. ⟨hal-03345646⟩
  • Ikram Chraibi Kaadoud, Lina Fahed. Intelligence artificielle explicable : vers des systèmes transparents, acceptables et éthiques. 2021. ⟨hal-03345679⟩