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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 2
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Blog Post number 1
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portfolio
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publications
Highly Fast Text Segmentation With Pairwise Markov Chains
Published in 6th IEEE Congress on Information Science and Technology (CiSt), 2020
This paper is about applying the Pairwise Markov Chain model for text segmentation (Chunking, Part-Of-Speech tagging, Named-Entity-Recognition).
Recommended citation: E. Azeraf, E. Monfrini, E. Vignon and W. Pieczynski, "Highly Fast Text Segmentation With Pairwise Markov Chains," 2020 6th IEEE Congress on Information Science and Technology (CiSt), 2020, pp. 361-366, doi: 10.1109/CiSt49399.2021.9357304. https://arxiv.org/pdf/2102.11037.pdf
Introducing the Hidden Neural Markov Chain framework
Published in 13th International Conference on Agents and Artificial Intelligence (ICAART), 2021
This paper introduces the Hidden Neural Markov Chain framework with application to text segmentation tasks and comparison with RNN anc BiRNN models.
Recommended citation: Azeraf, E.; Monfrini, E.; Vignon, E. and Pieczynski, W. (2021). Introducing the Hidden Neural Markov Chain Framework. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8; ISSN 2184-433X, pages 1013-1020. DOI: 10.5220/0010303310131020 https://arxiv.org/pdf/2102.11038
Using the Naive Bayes as a discriminative classifier
Published in 13th International Conference on Machine Learning and Computing (ICMLC), 2021
This paper shows how to define the classifier induced from the Naive Bayes as a discriminative classifier and the comparison between the Naive Bayes and the Logistic Regression.
Recommended citation: Elie Azeraf, Emmanuel Monfrini, and Wojciech Pieczynski. 2021. Using the Naive Bayes as a discriminative model. In 2021 13th International Conference on Machine Learning and Computing (ICMLC 2021). Association for Computing Machinery, New York, NY, USA, 106–110. DOI:https://doi.org/10.1145/3457682.3457697 https://arxiv.org/abs/2012.13572
teaching
Introduction to Statistics
Teaching assitant, L3 Course, ENSAE Paris, Institut Polytechnique de Paris, 2020
Course link.
Introduction to Stochastic Processes
Teaching assistant, M1 course, ENSAE Paris, Institut Polytechnique de Paris, 2020
Course link.