11 hasil untuk "Guyon, Isabelle"
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Artikel Jurnal Teks lengkap
Systematic aware learning
Experimental science often has to cope with systematic errors that coherently bias data. We analyze this issue on the analysis of data produced by experiments of the Large Hadron Collider at CERN as a case of supervised domain adaptation. Systematics-aware learning should create an efficient…
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Artikel Jurnal Teks lengkap
The Problem of Fairness in Synthetic Healthcare Data
Access to healthcare data such as electronic health records (EHR) is often restricted by laws established to protect patient privacy. These restrictions hinder the reproducibility of existing results based on private healthcare data and also limit new research. Synthetically-generated healthcare…
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Artikel Jurnal Teks lengkap
Bridging the Gap of AutoGraph Between Academia and Industry: Analyzing AutoGraph Challenge at KDD Cup 2020
Graph structured data is ubiquitous in daily life and scientific areas and has attracted increasing attention. Graph Neural Networks (GNNs) have been proved to be effective in modeling graph structured data and many variants of GNN architectures have been proposed. However, much human effort is…
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Artikel Jurnal Teks lengkap
Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform
Obtaining a standardized benchmark of computational methods is a major issue in data-science communities. Dedicated frameworks enabling fair benchmarking in a unified environment are yet to be developed. Here, we introduce Codabench, a meta-benchmark platform that is open sourced and community…
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Artikel Jurnal Teks lengkap
DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification
BACKGROUND: Quantification of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specificities. Bioinformatic tools to assess the different cell populations from single-omic datasets as bulk transcriptome or methylome samples have…
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Buku Teks lengkap
Cause Effect Pairs in Machine Learning
This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by machine learning…
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Artikel Jurnal Teks lengkap
The TrackML high-energy physics tracking challenge on Kaggle
The High-Luminosity LHC (HL-LHC) is expected to reach unprecedented collision intensities, which in turn will greatly increase the complexity of tracking within the event reconstruction. To reach out to computer science specialists, a tracking machine learning challenge (TrackML) was set up on…
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Artikel Jurnal
Track reconstruction at LHC as a collaborative data challenge use case with RAMP
Charged particle track reconstruction is a major component of data-processing in high-energy physics experiments such as those at the Large Hadron Collider (LHC), and is foreseen to become more and more challenging with higher collision rates. A simplified two-dimensional version of the track…
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Preprint Teks lengkap
FAIR Universe Weak Lensing ML Uncertainty Challenge: Handling Uncertainties and Distribution Shifts for Precision Cosmology
Weak gravitational lensing, the correlated distortion of background galaxy shapes by foreground structures, is a powerful probe of the matter distribution in our universe and allows accurate constraints on the cosmological model. In recent years, high-order statistics and machine learning (ML)…
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Artikel Jurnal Teks lengkap
FAIR Universe 2024: Higgs ML Uncertainty Challenge
The HiggsML Uncertainty Challenge is a machine learning competition aimed at improving uncertainty-aware AI techniques in high-energy physics. Part of the FAIR Universe initiative, focuses on estimating the Higgs boson signal strength while accounting for systematic uncertainties affecting collider…
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Buku Teks lengkap
Explainable and interpretable models in computer vision and machine learning
This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning.Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like…