Publications related to Neural network software

Deep Neural Networks With Trainable Activations and Controlled Lipschitz Constant

Michaël Unser, Shayan Aziznejad, Harshit Gupta, Joaquim Gonçalves Garcia Barreto Campos

We introduce a variational framework to learn the activation functions of deep neural networks. Our aim is to increase the capacity of the network while controlling an upper-bound of the actual Lipschitz constant of the input-output relation. To that end, ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Learning approaches to high-fidelity optical diffraction tomography

Joowon Lim

Optical diffraction tomography (ODT) provides us 3D refractive index (RI) distributions of transparent samples. Since RI values differ across different materials, they serve as endogenous contrasts. It, therefore, enables us to image without pre-processing ...

EPFL2020

Feature Learning of Virus Genome Evolution With the Nucleotide Skip-Gram Neural Network

Hyunjin Shim

Recent studies reveal that even the smallest genomes such as viruses evolve through complex and stochastic processes, and the assumption of independent alleles is not valid in most applications. Advances in sequencing technologies produce multiple time-poi ...

2019

Non-intrusive reduced order modeling of nonlinear problems using neural networks

Jan Sickmann Hesthaven, Stefano Ubbiali

We develop a non-intrusive reduced basis (RB) method for parametrized steady-state partial differential equations (PDEs). The method extracts a reduced basis from a collection of high-fidelity solutions via a proper orthogonal decomposition (POD) and emplo ...

2018

Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem

Qian Wang, Jan Sickmann Hesthaven, Deep Ray

A non-intrusive reduced-basis (RB) method is proposed for parametrized unsteady flows. A set of reduced basis functions are extracted from a collection of high-fidelity solutions via a proper orthogonal decomposition (POD), and the coefficients of the redu ...

2018

Learning to Assign Orientations to Feature Points

Pascal Fua, Vincent Lepetit, Kwang Moo Yi, Yannick Verdie

We show how to train a Convolutional Neural Network to assign a canonical orientation to feature points given an image patch centered on the feature point. Our method improves feature point matching upon the state-of-the art and can be used in conjunction ...

2016

Multitask Diffusion Adaptation Over Networks

Ali H. Sayed, Jie Chen

Adaptive networks are suitable for decentralized inference tasks. Recent works have intensively studied distributed optimization problems in the case where the nodes have to estimate a single optimum parameter vector collaboratively. However, there are man ...

Institute of Electrical and Electronics Engineers2014

Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem

Qian Wang, Jan Sickmann Hesthaven, Deep Ray

A non-intrusive reduced-basis (RB) method is proposed for parametrized unsteady flows. A set of reduced basis functions are extracted from a collection of high-fidelity solutions via a proper orthogonal decomposition (POD), and the coefficients of the redu ...

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Neural network software

Deep Neural Networks With Trainable Activations and Controlled Lipschitz Constant

Learning approaches to high-fidelity optical diffraction tomography

Feature Learning of Virus Genome Evolution With the Nucleotide Skip-Gram Neural Network

Non-intrusive reduced order modeling of nonlinear problems using neural networks

Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem

Learning to Assign Orientations to Feature Points

Multitask Diffusion Adaptation Over Networks

Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem

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Deep Neural Networks With Trainable Activations and Controlled Lipschitz Constant

Feature Learning of Virus Genome Evolution With the Nucleotide Skip-Gram Neural Network

Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem

Learning approaches to high-fidelity optical diffraction tomography

Non-intrusive reduced order modeling of nonlinear problems using neural networks

Multitask Diffusion Adaptation Over Networks

Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem

Learning to Assign Orientations to Feature Points