Simulated annealing

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Generalized parallel tempering on Bayesian inverse problems

Fabio Nobile, Juan Pablo Madrigal Cianci

In the current work we present two generalizations of the Parallel Tempering algorithm in the context of discrete-timeMarkov chainMonteCarlo methods for Bayesian inverse problems. These generalizations use state-dependent swapping rates, inspired by the so ...

2021

Turning PathFinder Upside -Down: 1-4,xploring FPGA Switch -Blocks by Negotiating Switch Presence

Paolo Ienne, Stefan Nikolic

Automated switch-block exploration gains in importance as technology scaling brings more emphasis on the physical constraints, making it insufficient to rely on abstract measures of routability alone. In this work, we take an approach that significantly di ...

IEEE COMPUTER SOC2021

Exploring ferroelectricity in HfO2-based thin films by tackling application-relevant challenges

Matteo Cavalieri

Ferroelectric materials offer a broad range of application-relevant properties, including spontaneous polarization switchable by electric field. Archetypical representatives of this class of materials are perovskites, currently used in applications ranging ...

EPFL2021

Active Subdivision Surfaces for the Semiautomatic Segmentation of Biomedical Volumes

Michaël Unser, Anaïs Laure Marie-Thérèse Badoual

We present a new family of active surfaces for the semiautomatic segmentation of volumetric objects in 3D biomedical images. We represent our deformable model by a subdivision surface encoded by a small set of control points and generated through a geometr ...

2021

Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information

Giuseppe Carleo, Julien Sebastian Gacon, Stefan Woerner

The Quantum Fisher Information matrix (QFIM) is a central metric in promising algorithms, such as Quantum Natural Gradient Descent and Variational Quantum Imaginary Time Evolution. Computing the full QFIM for a model with d parameters, however, is computat ...

VEREIN FORDERUNG OPEN ACCESS PUBLIZIERENS QUANTENWISSENSCHAF2021

Evaluating a prediction system for snow management

Michael Lehning

The evaluation of snowpack models capable of accounting for snow management in ski resorts is a major step towards acceptance of such models in supporting the daily decision-making process of snow production managers. In the framework of the EU Horizon 202 ...

COPERNICUS GESELLSCHAFT MBH2021

A Global-Local Approach For Detecting Hotspots In Multiple-Response Regression

Anthony Christopher Davison, Hélène Ruffieux, Jörg Hager

We tackle modelling and inference for variable selection in regression problems with many predictors and many responses. We focus on detecting hotspots, that is, predictors associated with several responses. Such a task is critical in statistical genetics, ...

INST MATHEMATICAL STATISTICS2020

Active Improvement of Control Policies with Bayesian Gaussian Mixture Model

Sylvain Calinon, Emmanuel Pignat, Hakan Girgin, Noémie Laure Gwendoline Jaquier

Learning from demonstration (LfD) is an intuitive framework allowing non-expert users to easily (re-)program robots. However, the quality and quantity of demonstrations have a great influence on the generalization performances of LfD approaches. In this pa ...

IEEE2020

Stabilization and Performance Improvement of Power-Hardware-in-the-Loop Test Using a Robust Data-Driven Method

Alireza Karimi, Seyed Sohail Madani

Power-Hardware-in-the-Loop (PHIL) setups have gained high importance in validation of the performance of newly developed instruments and devices with low risk and implementation cost. However, the interconnection of a power hardware with a simulated model ...

2022

On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent

Nicolas Henri Bernard Flammarion, Scott William Pesme, Aymeric Daphnis Kévin Dieuleveut

Constant step-size Stochastic Gradient Descent exhibits two phases: a transient phase during which iterates make fast progress towards the optimum, followed by a stationary phase during which iterates oscillate around the optimal point. In this paper, we s ...

2020

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