Výsledky vyhledávání - "Renard, Benjamin"

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Event Detection in Time Series: Universal Deep Learning Approach

Autor: Azib, Menouar, Renard, Benjamin, Garnier, Philippe, Génot, Vincent, André, Nicolas

Event detection in time series is a challenging task due to the prevalence of imbalanced datasets, rare events, and time interval-defined events. Traditional supervised deep learning methods primarily employ binary classification, where each time ste

Externí odkaz: http://arxiv.org/abs/2311.15654

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A Comprehensive Python Library for Deep Learning-Based Event Detection in Multivariate Time Series Data and Information Retrieval in NLP

Autor: Azib, Menouar, Renard, Benjamin, Garnier, Philippe, Génot, Vincent, André, Nicolas

Event detection in time series data is crucial in various domains, including finance, healthcare, cybersecurity, and science. Accurately identifying events in time series data is vital for making informed decisions, detecting anomalies, and predictin

Externí odkaz: http://arxiv.org/abs/2310.16485

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Learning Regionalization using Accurate Spatial Cost Gradients within a Differentiable High-Resolution Hydrological Model: Application to the French Mediterranean Region

Autor: Huynh, Ngo Nghi Truyen, Garambois, Pierre-André, Colleoni, François, Renard, Benjamin, Roux, Hélène, Demargne, Julie, Jay-Allemand, Maxime, Javelle, Pierre

Estimating spatially distributed hydrological parameters in ungauged catchments poses a challenging regionalization problem and requires imposing spatial constraints given the sparsity of discharge data. A possible approach is to search for a transfe

Externí odkaz: http://arxiv.org/abs/2308.02040

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Multi-gauge Hydrological Variational Data Assimilation: Regionalization Learning with Spatial Gradients using Multilayer Perceptron and Bayesian-Guided Multivariate Regression

Autor: Huynh, Ngo Nghi Truyen, Garambois, Pierre-André, Colleoni, François, Renard, Benjamin, Roux, Hélène

Publikováno v: Colloque SHF 2023 - Pr{\'e}vision des crues et des inondations, Nov 2023, Toulouse, France

Tackling the difficult problem of estimating spatially distributed hydrological parameters, especially for floods on ungauged watercourses, this contribution presents a novel seamless regionalization technique for learning complex regional transfer f

Externí odkaz: http://arxiv.org/abs/2307.02497

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Positional Paper: Schema-First Application Telemetry

Autor: Shkuro, Yuri, Renard, Benjamin, Singh, Atul

Publikováno v: ACM SIGOPS Operating Systems Review, Volume 56, Issue 1, June 2022, pp 8-17

Application telemetry refers to measurements taken from software systems to assess their performance, availability, correctness, efficiency, and other aspects useful to operators, as well as to troubleshoot them when they behave abnormally. Many mode

Externí odkaz: http://arxiv.org/abs/2206.11380

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