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Alexandre Gramfort

Bio: Alexandre Gramfort is a academic researcher from Université Paris-Saclay. The author has contributed to research in topic(s): Coordinate descent & Optimization problem. The author has an hindex of 48, co-authored 258 publication(s) receiving 74203 citation(s). Previous affiliations of Alexandre Gramfort include Commissariat à l'énergie atomique et aux énergies alternatives & IBM.

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Papers
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Open accessJournal Article
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.

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33,540 Citations


Open accessPosted Content
02 Jan 2012-arXiv: Learning
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from this http URL.

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28,898 Citations


Open accessJournal ArticleDOI: 10.1016/J.NEUROIMAGE.2013.10.027
01 Feb 2014-NeuroImage
Abstract: Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals originating from neural currents in the brain Using these signals to characterize and locate brain activity is a challenging task, as evidenced by several decades of methodological contributions MNE, whose name stems from its capability to compute cortically-constrained minimum-norm current estimates from M/EEG data, is a software package that provides comprehensive analysis tools and workflows including preprocessing, source estimation, time–frequency analysis, statistical analysis, and several methods to estimate functional connectivity between distributed brain regions The present paper gives detailed information about the MNE package and describes typical use cases while also warning about potential caveats in analysis The MNE package is a collaborative effort of multiple institutes striving to implement and share best methods and to facilitate distribution of analysis pipelines to advance reproducibility of research Full documentation is available at http://martinosorg/mne

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1,099 Citations


Open accessJournal ArticleDOI: 10.3389/FNINS.2013.00267
Abstract: Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific comptutation (NumPy, SciPy) and visualization (matplotlib and Mayavi), as well as the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD license allowing code reuse, even in commercial products. Although MNE-Python has only been under heavy development for a couple of years, it has rapidly evolved with expanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers. Full documentation, including dozens of examples, is available at http://martinos.org/mne.

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Topics: Python (programming language) (59%), NumPy (59%), Scripting language (53%) ...read more

953 Citations


Open accessJournal ArticleDOI: 10.3389/FNINF.2014.00014
Abstract: Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g. resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.

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897 Citations


Cited by
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Open accessJournal Article
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.

...read more

33,540 Citations


Open accessPosted Content
02 Jan 2012-arXiv: Learning
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from this http URL.

...read more

28,898 Citations


Open accessProceedings ArticleDOI: 10.1145/2939672.2939785
Tianqi Chen1, Carlos Guestrin1Institutions (1)
13 Aug 2016-
Abstract: Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.

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Topics: Incremental decision tree (64%), Gradient boosting (61%), ID3 algorithm (60%) ...read more

10,428 Citations


Open access
Christopher M. Bishop1Institutions (1)
01 Jan 2006-
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

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Topics: Kernel method (60%), Kernel (statistics) (60%), Graphical model (58%) ...read more

10,141 Citations


Open accessBook
01 Jan 2009-

8,216 Citations


Performance
Metrics

Author's H-index: 48

No. of papers from the Author in previous years
YearPapers
202129
202029
201931
201827
201725
201615

Top Attributes

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Author's top 5 most impactful journals

arXiv: Machine Learning

40 papers, 726 citations

arXiv: Learning

16 papers, 29.7K citations

NeuroImage

15 papers, 2.1K citations

bioRxiv

11 papers, 35 citations

medRxiv

7 papers, 74 citations

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