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Publications associées (16)
Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain
Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...
EPFL2024Genuinely distributed Byzantine machine learning
Rachid Guerraoui, El Mahdi El Mhamdi, Le Nguyen Hoang, Sébastien Louis Alexandre Rouault, Arsany Hany Abdelmessih Guirguis
Machine learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train the model. Clearly, such architecture is prone to various types of component ...
2022Motivating Innovation: The Effect of Loss Aversion on the Willingness to Persist
We investigate the willingness of individuals to persist at exploration in the face of failure. Prior research suggests that the organization's "tolerance for failure" may motivate greater exploration by the individual. Little is known, however, about how ...
MIT Press2020Multi-Objective Façade Optimization for Daylighting Design Using a Genetic Algorithm
A building’s facade design has significant impact on the daylighting performance of interior spaces. This paper presents a tool based on a genetic algorithm (GA) which facilitates exploration of facade designs generated based on illuminance and/or glare ob ...
2010