Factorisation Matricielle Nonnégative rapide dans des espaces de Hilbert

La factorisation matricielle nonnégative (FMN) est une méthode populaire d’analyse de données consistant à exprimer les données comme une combinaison linéaire nonnégative d’un petit nombre de facteurs caractéristiques nonnégatifs. Cela permet de réduire la taille des données, de filtrer le bruit présent dans celles-ci et de mieux les comprendre et les analyser. Cette technique possède […]

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Online Data Imputation by Modified Mixture Density Networks

In the era of big data, software based on artificial intelligence has greatly improved the quality of people’s lives. Although data is not a scarce resource, the data we collected are usually incomplete due to many reasons, i.e. they contain some missing values. Simply deleting these missing data will not only cause great waste, but […]

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Fraud Detection in Derivatives Market using Graph Neural Network (GNN)

In the last few years, a high increase in the interest of traders and investors towards financial instruments directly led to an important augmentation of the information received daily by exchanges. Exchange regulators, who constantly monitor markets to unveil potential infractions, traditionally perform their investigation manually and the notable growth in market activity represents an […]

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Équité et discrimination des modèles en assurance, un état de l’art

Historiquement, les primes d’assurance ont été ainsi différenciées à l’aide de quelques variables tarifaires, comme la surface de la maison en assurance habitation ou la puissance de la voiture en assurance automobile, mais aussi du genre ou de l’âge de l’assuré. Mais si la corrélation globale entre âge et coût du risque est indéniable dans […]

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Discovering novel approaches to robust machine learning and visualization for banking applications

The overall objective of this project is to develop approaches to improve rating robustness that are distributionally robust. We will develop techniques to utilize ensemble learning machine learning models with categorical monotonic constraints. Lastly, we will develop novel data visualization tools for business intelligence tasks that will help decision makers at Scotia Bank.

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Human Health Risk Assessment of Manganese and Inorganic Manganese Compounds and the Application of Categorical Regression in the Quantitative Risk Assessment of Manganese

Risk Sciences International is currently completing a comprehensive risk assessment of the potential human health effects of manganese. This assessment involves a systematic review of the worlds’ literature on epidemiological and toxicological studies of manganese, following which an international expert panel has scored all of the adverse health outcomes identified through this review using a […]

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Economic model of Ulipristal Acetate in the long term, intermittent treatment of uterine fibroids — a Canadian setting

Health economic evaluation compares the associated costs and the clinical outcomes of multiple treatment alternatives and is presently used by payers as one of the many types of evidence to inform which drugs to fund. Given existing health-care budgetary constraints, payers are increasingly interested in knowing whether a new health technology will provide value (i.e. […]

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Blackbox optimization applied to Design of Experiments

The pharmaceutical industry relies on a process known as batch manufacturing to supply their goods to the public. Each batch takes time to ends, involves many parameters and is very expensive. Quality tests are performed at the end of each batch. The calibration of parameters is a crucial task and Design of Experiment (DoE) is […]

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Evaluation of Machine Learning Methods for Portfolio Replication of VIX Futures

During the past two decades, the CBOE Volatility Index (VIX® Index), a key measure of investor sentiment and 30-day future volatility expectations, has generated much investor attention because of its unique and powerful features. The introduction of VIX futures in 2004, VIX options in 2006, and other volatility-related trading instruments provided traders and investors access […]

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Fraud Detection in Derivatives Market using Deep Unsupervised Anomaly Detection and NLP

In the last few years, a high increase in the interest of traders and investors towards financial instruments directly led to an important augmentation of the information received daily by exchanges. Exchange regulators, who constantly monitor markets to unveil potential infractions, traditionally perform their investigation manually and the notable growth in market activity represents an […]

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Large-scale Inversion of geophysical data

When using geophysical methods to gain insight into the structure of earth, large geophysical data sets are collected. Since the earth is a 3D structure, the data must be interpreted and processed in 3D to be of the most value in the exploration process. This research will develop the capability to invert large gravity, magnetics, […]

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