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Research

Advestis part of R&D Forvis Mazars has been developing and operating ML algorithms
in commercial production since 2013.

Explore our Research below

Rechercher

Using Non-negative matrix factorization to classify companies

Using Non-negative matrix factorization to classify companies

Using Non-negative matrix factorization to classify companies

C. Geissler, Towards Data Science, October 1st, 2020.

Advestis

1 oct. 2020

A rigorous method to compare interpretability of rule-based algorithms

A rigorous method to compare interpretability of rule-based algorithms

A rigorous method to compare interpretability of rule-based algorithms

V. Margot, Preprint, 2020.

Advestis

23 sept. 2020

Speed Cubing for Machine Learning - Episode 1

Speed Cubing for Machine Learning - Episode 1

Speed Cubing for Machine Learning - Episode 1

N. Morizet, Towards Data Science, September 15th, 2020.

Advestis

15 sept. 2020

Introduction to Generative Adversarial Networks

Introduction to Generative Adversarial Networks

Introduction to Generative Adversarial Networks

N. Morizet, Advestis Tech Report, July 2020.

Advestis

15 juil. 2020

A self-learning and interpretable investment strategy based on extra-financial data

A self-learning and interpretable investment strategy based on extra-financial data

A self-learning and interpretable investment strategy based on extra-financial data

C. Geissler, V. Margot, Chaire DAMI, September 2020.

Advestis

27 avr. 2020

PROCÉDÉ DE PRÉDICTION INTERPRÉTABLE PAR APPRENTISSAGE FONCTIONNANT SOUS RESSOURCES MÉMOIRES LIMITÉES

PROCÉDÉ DE PRÉDICTION INTERPRÉTABLE PAR APPRENTISSAGE FONCTIONNANT SOUS RESSOURCES MÉMOIRES LIMITÉES

PROCÉDÉ DE PRÉDICTION INTERPRÉTABLE PAR APPRENTISSAGE FONCTIONNANT SOUS RESSOURCES MÉMOIRES LIMITÉES

N° et date de dépôt : FR2003637 - 10/04/2020

Advestis

10 avr. 2020

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