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Publications associées (28)

Deep Spline Networks With Control Of Lipschitz Regularity

Michaël Unser, Shayan Aziznejad

The motivation for this work is to improve the performance of deep neural networks through the optimization of the individual activation functions. Since the latter results in an infinite-dimensional optimization problem, we resolve the ambiguity by search ...

IEEE2019

Phenotypic redshifts with self-organizing maps: A novel method to characterize redshift distributions of source galaxies for weak lensing

Eduardo Sanchez, Romain Buchs

Wide-field imaging surveys such as the Dark Energy Survey (DES) rely on coarse measurements of spectral energy distributions in a few filters to estimate the redshift distribution of source galaxies. In this regime, sample variance, shot noise, and selecti ...

2019

A component-based hybrid reduced basis/finite element method for solid mechanics with local nonlinearities

Jonas Ballani

The SCRBE (Static-Condensation Reduced-Basis-Element) method is a component-to-system model order reduction approach for efficient many-query and real-time treatment of linear partial differential equations characterized by many spatially distributed const ...

ELSEVIER SCIENCE SA2018

New Multi-Keyword Ciphertext Search Method for Sensor Network Cloud Platforms

Jiyong Zhang, Hongyu Yang

This paper proposed a multi-keyword ciphertext search, based on an improved-quality hierarchical clustering (MCS-IQHC) method. MCS-IQHC is a novel technique, which is tailored to work with encrypted data. It has improved search accuracy and can self-adapt ...

MDPI2018

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

Clément Hongler, Franck Raymond Gabriel, Arthur Ulysse Jacot-Guillarmod

At initialization, artificial neural networks (ANNs) are equivalent to Gaussian processes in the infinite-width limit [12, 9], thus connecting them to kernel methods. We prove that the evolution of an ANN during training can also be described by a kernel: ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Learning with tensors: a framework based on convex optimization and spectral regularization

Quoc Tran Dinh

We present a framework based on convex optimization and spectral regularization to perform learning when feature observations are multidimensional arrays (tensors). We give a mathematical characterization of spectral penalties for tensors and analyze a uni ...

Springer2014

On Lightning Electromagnetic Field Propagation Along an Irregular Terrain

Farhad Rachidi-Haeri, Marcos Rubinstein, Mario Paolone, Davide Pavanello, Mohammad Azadifar, Zhaoyang Wang, Stefan Metz

In this paper, we present a theoretical analysis of the propagation effects of lightning electromagnetic fields over a mountainous terrain. The analysis is supported by experimental observations consisting of simultaneous records of lightning currents and ...

Institute of Electrical and Electronics Engineers2016

Efficient Reduction Techniques for the Simulation and Optimization of Parametrized Systems : Analysis and Applications

Federico Negri

This thesis is concerned with the development, analysis and implementation of efficient reduced order models (ROMs) for the simulation and optimization of parametrized partial differential equations (PDEs). Indeed, since the high-fidelity approximation of ...

EPFL2015