Author
Bertrand Thirion
Other affiliations: French Institute for Research in Computer Science and Automation, French Institute of Health and Medical Research, École Polytechnique ...read more
Bio: Bertrand Thirion is an academic researcher from Université Paris-Saclay. The author has contributed to research in topics: Cluster analysis & Cognition. The author has an hindex of 51, co-authored 311 publications receiving 73839 citations. Previous affiliations of Bertrand Thirion include French Institute for Research in Computer Science and Automation & French Institute of Health and Medical Research.
Papers published on a yearly basis
Papers
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08 Sep 2015
TL;DR: In this paper, a variant of FISTA, called fAASTA, is presented, which relies on an internal solver for the TV proximal operator, and refines its tolerance to balance computational cost of the gradient and the proximal steps.
Abstract: The total variation (TV) penalty, as many other
analysis-sparsity problems, does not lead to separable factors or a proximal operator
with a closed-form expression, such as soft thresholding for the $\ell_1$ penalty.
As a result, in a variational formulation of an inverse problem or statistical
learning estimation, it leads to challenging non-smooth optimization problems
that are often solved with elaborate single-step first-order methods. When the
data-fit term arises from empirical measurements, as in brain imaging, it is
often very ill-conditioned and without simple structure. In this situation,
in proximal splitting methods, the computation cost of the
gradient step can easily dominate each iteration. Thus it is beneficial
to minimize the number of gradient steps.
We present fAASTA, a variant of FISTA, that relies on an internal solver for
the TV proximal operator, and refines its tolerance to balance computational
cost of the gradient and the proximal steps. We give benchmarks and
illustrations on ``brain decoding'': recovering brain maps from noisy
measurements to predict observed behavior. The algorithm as well as the
empirical study of convergence speed are valuable for any non-exact proximal
operator, in particular analysis-sparsity problems.
4 citations
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24 May 2019TL;DR: This article proposed a denoising regularizer to leverage structure in the data in a way that can be applied efficiently to complex models by using a fast clustering algorithm inside a stochas-tic gradient descent loop.
Abstract: In many applications where collecting data is expensive , for example neuroscience or medical imaging, the sample size is typically small compared to the feature dimension. It is challenging in this setting to train expressive, non-linear models without overfitting. These datasets call for intelligent regularization that exploits known structure, such as correlations between the features arising from the measurement device. However, existing structured regularizers need specially crafted solvers, which are difficult to apply to complex models. We propose a new regularizer specifically designed to leverage structure in the data in a way that can be applied efficiently to complex models. Our approach relies on feature grouping, using a fast clustering algorithm inside a stochas-tic gradient descent loop: given a family of feature groupings that capture feature covariations, we randomly select these groups at each iteration. We show that this approach amounts to enforcing a denoising regularizer on the solution. The method is easy to implement in many model archi-tectures, such as fully connected neural networks, and has a linear computational cost. We apply this regularizer to a real-world fMRI dataset and the Olivetti Faces datasets. Experiments on both datasets demonstrate that the proposed approach produces models that generalize better than those trained with conventional regularizers, and also improves convergence speed.
4 citations
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TL;DR: The concept of functional fingerprint is introduced in this article, which subsumes the accumulation of functional information at a given brain location, which is discussed in detail through concrete examples taken from the Individual Brain Charting dataset.
Abstract: How can neuroimaging inform us about the function of brain structures? This simple question immediately brings out two pertinent issues: (i) an inference problem, namely the fact that the function of a region can only be asserted after observing a large array of experimental conditions or contrasts; and (ii) the fact that the identity of a region can only be defined with accuracy at the individual level, because of intrinsic differences between subjects. To overcome this double challenge, we consider an approach based on the deep phenotyping of behavioral responses from task data acquired using functional Magnetic Resonance Imaging. The concept of functional fingerprint-which subsumes the accumulation of functional information at a given brain location-is herein discussed in detail through concrete examples taken from the Individual Brain Charting dataset.
4 citations
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20 Sep 2010
TL;DR: A novel method, Multi-Class Sparse Bayesian Regression (MCBR), that generalizes classical approaches such as Ridge regression and Automatic Relevance Determination and achieves similar prediction accuracies than reference methods, and yields more interpretable feature loadings.
Abstract: The use of machine learning tools is gaining popularity in neuroimaging, as it provides a sensitive assessment of the information conveyed by brain images. In particular, finding regions of the brain whose functional signal reliably predicts some behavioral information makes it possible to better understand how this information is encoded or processed in the brain. However, such a prediction is performed through regression or classification algorithms that suffer from the curse of dimensionality, because a huge number of features (i.e. voxels) are available to fit some target, with very few samples (i.e. scans) to learn the informative regions. A commonly used solution is to regularize the weights of the parametric prediction function. However, model specification needs a careful design to balance adaptiveness and sparsity. In this paper, we introduce a novel method, Multi-Class Sparse Bayesian Regression (MCBR), that generalizes classical approaches such as Ridge regression and Automatic Relevance Determination. Our approach is based on a grouping of the features into several classes, where each class is regularized with specific parameters. We apply our algorithm to the prediction of a behavioral variable from brain activation images. The method presented here achieves similar prediction accuracies than reference methods, and yields more interpretable feature loadings.
3 citations
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TL;DR: In this paper, the Ensemble of Clustered Desparsified Lasso (EnCluDL) procedure for multivariate statistical inference on high-dimensional structured data was proposed.
3 citations
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TL;DR: 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, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
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.
47,974 citations
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TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
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.
28,898 citations
28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。
18,940 citations
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13 Aug 2016TL;DR: XGBoost as discussed by the authors proposes a sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning to achieve state-of-the-art results on many machine learning challenges.
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.
14,872 citations
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TL;DR: This paper proposes a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning and provides insights on cache access patterns, data compression and sharding to build a scalable tree boosting system called XGBoost.
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.
13,333 citations