Paris : Pihan Delaforest (Morinval), 1831.

Paris : Delahaye, 1877.

Paris : O. Doin, 1887.

Paris : J.-B. Baillière, 1877.

Paris : J.-B. Baillière, 1878.

Paris : J.-B. Baillière, 1878.

Londres : Paris, 1840.

Paris : G. Steinheil, 1891.

Paris : G. Steinheil, 1892.

Paris : Steinheil, 1896.

Paris : Asselin et Houzeau, 1892.

Paris : Steinheil, 1899.

Paris : O. Doin, 1881.

Paris : Germer Bailliere, 1878.

Paris : J.-B. Baillière, 1879.

Paris : Société d’éditions scientifiques, 1895.

Bruxelles : A. Manceaux, 1893.

Paris : O. Doin, 1884.

Paris : Rémont, 1815.

Paris : Germer-Bailliere, 1877.

Paris : V. Masson, 1867.

Paris : chez Magimel, 1815.

London : Longmans, Green, 1868.

Paris : Société d’éditions scientifiques, 1896.

Paris : Germer-Bailliere, 1856-1857.

Londres : H. Bailliere, 1843-1847.

Bruxelles : F. Hayez, 1890.

London : Philadelphia, 1890.

Paris : Alcan, 1903.

Toulon : chez l'auteur, 1812.

Paris : Steinheil, 1896.

Paris : Parent, 1874.

Paris : J.-B. Baillière, 1905.

Londres : Paris, 1866.

Paris : Société d’éditions scientifiques, 1901.

Paris : Fortin, Masson et Ce, 1843.

Londres : Paris, 1840.

Paris : A. Delahaie, 1872.

Paris : G. Steinheil, 1895.

Paris : Béchet jeune, 1838.

Paris : chez Mequignon-Marvis, 1816.

A Paris : Chez Iean Dhourry, au bout du Pont-Neuf, près les Augustins, à l'Image S. Iean, M. DC. LXI. [1661]

Paris, [France] : J.B. Baillière, Libraire de l'Académie Impériale de Médicine, 1854.

Paris, [France] : J.B. Baillière, Libraire de l'Académie Impériale de Médicine, 1856.

Rockville, Maryland : National Institute on Drug Abuse, 1986.

Rockville, Maryland : National Institute on Drug Abuse, 1986.

Karl Ewald, Ulrike Schneider.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 944--969.

Abstract:
We derive expressions for the finite-sample distribution of the Lasso estimator in the context of a linear regression model in low as well as in high dimensions by exploiting the structure of the optimization problem defining the estimator. In low dimensions, we assume full rank of the regressor matrix and present expressions for the cumulative distribution function as well as the densities of the absolutely continuous parts of the estimator. Our results are presented for the case of normally distributed errors, but do not hinge on this assumption and can easily be generalized. Additionally, we establish an explicit formula for the correspondence between the Lasso and the least-squares estimator. We derive analogous results for the distribution in less explicit form in high dimensions where we make no assumptions on the regressor matrix at all. In this setting, we also investigate the model selection properties of the Lasso and show that possibly only a subset of models might be selected by the estimator, completely independently of the observed response vector. Finally, we present a condition for uniqueness of the estimator that is necessary as well as sufficient.

We study the problem of globally recovering a dictionary from a set of signals via $ell_1$-minimization. We assume that the signals are generated as i.i.d. random linear combinations of the $K$ atoms from a complete reference dictionary $D^*in mathbb R^{K imes K}$, where the linear combination coefficients are from either a Bernoulli type model or exact sparse model. First, we obtain a necessary and sufficient norm condition for the reference dictionary $D^*$ to be a sharp local minimum of the expected $ell_1$ objective function. Our result substantially extends that of Wu and Yu (2015) and allows the combination coefficient to be non-negative. Secondly, we obtain an explicit bound on the region within which the objective value of the reference dictionary is minimal. Thirdly, we show that the reference dictionary is the unique sharp local minimum, thus establishing the first known global property of $ell_1$-minimization dictionary learning. Motivated by the theoretical results, we introduce a perturbation based test to determine whether a dictionary is a sharp local minimum of the objective function. In addition, we also propose a new dictionary learning algorithm based on Block Coordinate Descent, called DL-BCD, which is guaranteed to decrease the obective function monotonically. Simulation studies show that DL-BCD has competitive performance in terms of recovery rate compared to other state-of-the-art dictionary learning algorithms when the reference dictionary is generated from random Gaussian matrices.

Methods for generating synthetic data have become of increasing importance to build large datasets required for Convolution Neural Networks (CNN) based deep learning techniques for a wide range of computer vision applications. In this work, we extend existing methodologies to show how 2D thermal facial data can be mapped to provide 3D facial models. For the proposed research work we have used tufts datasets for generating 3D varying face poses by using a single frontal face pose. The system works by refining the existing image quality by performing fusion based image preprocessing operations. The refined outputs have better contrast adjustments, decreased noise level and higher exposedness of the dark regions. It makes the facial landmarks and temperature patterns on the human face more discernible and visible when compared to original raw data. Different image quality metrics are used to compare the refined version of images with original images. In the next phase of the proposed study, the refined version of images is used to create 3D facial geometry structures by using Convolution Neural Networks (CNN). The generated outputs are then imported in blender software to finally extract the 3D thermal facial outputs of both males and females. The same technique is also used on our thermal face data acquired using prototype thermal camera (developed under Heliaus EU project) in an indoor lab environment which is then used for generating synthetic 3D face data along with varying yaw face angles and lastly facial depth map is generated.

Beginning from a basic neural-network architecture, we test the potential benefits offered by a range of advanced techniques for machine learning, in particular deep learning, in the context of a typical classification problem encountered in the domain of high-energy physics, using a well-studied dataset: the 2014 Higgs ML Kaggle dataset. The advantages are evaluated in terms of both performance metrics and the time required to train and apply the resulting models. Techniques examined include domain-specific data-augmentation, learning rate and momentum scheduling, (advanced) ensembling in both model-space and weight-space, and alternative architectures and connection methods.

Following the investigation, we arrive at a model which achieves equal performance to the winning solution of the original Kaggle challenge, whilst being significantly quicker to train and apply, and being suitable for use with both GPU and CPU hardware setups. These reductions in timing and hardware requirements potentially allow the use of more powerful algorithms in HEP analyses, where models must be retrained frequently, sometimes at short notice, by small groups of researchers with limited hardware resources. Additionally, a new wrapper library for PyTorch called LUMINis presented, which incorporates all of the techniques studied.