Dans cet article, nous introduisons une nouvelle approche pour analyser le processus de diffusion de nouveaux produits qui tient compte de l'hétérogénéité de...

We consider the iterative solution of regularized saddle-point systems. When the leading block is symmetric and positive semi-definite on an appropriate sub...

Nous développons des bornes sur les valeurs propres d’une nouvelle formulation des équations de Newton dans les méthodes de points intérieurs pour l’optimisa...

Dans cet article, nous appliquons une méthode de dispatching économique exploitant la flexibilité d’éléments de stockage d’énergie et de pilotage de charge...

The Mars Curiosity rover is frequently sending back engineering and science data that goes through a pipeline of systems before reaching its final destinati...

Column generation (CG) algorithms are well known to suffer from convergence issues due, mainly, to the degenerate structure of their master problem and the ...

We consider an integrated optimization problem including the production, inventory, and outbound transportation decisions where a central plant fulfills the ...

Restless bandits are a class of sequential resource allocation problems concerned with allocating one or more resources among several alternative processes...

A new business opportunity is emerging with the combination of three key market trends: (1) Increased penetration of residential solar PV; (2) Rapid reductio...

Utility-based shortfall risk measure (SR) effectively captures decision maker’s risk attitude on tail losses by an increasing convex loss function. In this ...

L'apprentissage profond a redéfini les normes modernes et la performance dans les domaines de la vision informatique et du traitement du langage. Avec l’ac...

Column generation (CG) is widely used for solving large-scale optimization problems. This article presents a new approach based on a machine learning (ML) t...

Artificial Intelligence (AI) is the next society transformation builder. Massive AI-based applications include cloud servers, cell phones, cars, and pandemic...

One of the major challenges in large-scale distributed machine learning involving stochastic gradient methods is the high cost of gradient communication ove...

Pruning methods for deep neural networks based on weight magnitude have shown promise in recent research. We propose a new, highly flexible approach to neura...

The past few years have seen the ability of cooperative Malware Detection Systems (MDS) to detect complex and unknown malware. In a cooperative setting, an M...

The design of compact deep neural networks is a crucial task to enable widespread adoption of deep neural networks in the real-world, particularly for edge a...

Knowledge distillation is a technique that consists in training a student network, usually of a low capacity, to mimic the representation space and the perfo...

In time-to-event data analysis, the main object of interest is the time elapsed between the occurrence of two ordered events, say \(E_1, E_2\). Sampling fr...

Training large-scale deep neural networks is a long, time-consuming operation, often requiring many GPUs to accelerate. In large models, the time spent loadi...