Publications associées à Mean percentage error

TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression

Mathieu Salzmann, Alexandre Massoud Alahi, Megh Hiren Shukla

Deep heteroscedastic regression involves jointly optimizing the mean and covariance of the predicted distribution using the negative log-likelihood. However, recent works show that this may result in sub-optimal convergence due to the challenges associated ...

2024

Glenohumeral joint force prediction with deep learning

Dominique Pioletti, Alexandre Terrier, Patrick Goetti, Philippe Büchler

Deep learning models (DLM) are efficient replacements for computationally intensive optimization techniques. Musculoskeletal models (MSM) typically involve resource-intensive optimization processes for determining joint and muscle forces. Consequently, DLM ...

London2024

Range-separated hybrid functionals for accurate prediction of band gaps of extended systems

Alfredo Pasquarello, Stefano Falletta, Jing Yang

In this work, we systematically evaluate the accuracy in band gap prediction of range-separated hybrid functionals on a large set of semiconducting and insulating materials and carry out comparisons with the performance of their global counterparts. We obs ...

NATURE PORTFOLIO2023

DELMEP: a deep learning algorithm for automated annotation of motor evoked potential latencies

Friedhelm Christoph Hummel, Claudia Bigoni, Nima Taherinejad

The analysis of motor evoked potentials (MEPs) generated by transcranial magnetic stimulation (TMS) is crucial in research and clinical medical practice. MEPs are characterized by their latency and the treatment of a single patient may require the characte ...

NATURE PORTFOLIO2023

Statistical limits of dictionary learning: Random matrix theory and the spectral replica method

Nicolas Macris, Jean François Emmanuel Barbier

We consider increasingly complex models of matrix denoising and dictionary learning in the Bayes-optimal setting, in the challenging regime where the matrices to infer have a rank growing linearly with the system size. This is in contrast with most existin ...

AMER PHYSICAL SOC2022

Novel spatiotemporal processing tools for body-surface potential map signals for the prediction of catheter ablation outcome in persistent atrial fibrillation

Jean-Marc Vesin, Adrian Luca, Etienne Pruvot

Background: Signal processing tools are required to efficiently analyze data collected in body-surface-potential map (BSPM) recordings. A limited number of such tools exist for studying persistent atrial fibrillation (persAF). We propose two novel, spatiot ...

FRONTIERS MEDIA SA2022

Worldwide performance evaluation of ground surface reflectance models

Christophe Ballif, Pierre-Jean Alet, Arttu Matias Tuomiranta

To improve the accuracy of bifacial gain estimation, recent radiative models of solar energy systems have abandoned the traditional assumption of isotropic ground-reflected radiance. However, surface reflectance itself is still commonly considered as a con ...

2021

Differential Entropy of the Conditional Expectation under Gaussian Noise

Michael Christoph Gastpar, Alper Köse, Ahmet Arda Atalik

This paper considers an additive Gaussian noise channel with arbitrarily distributed finite variance input signals. It studies the differential entropy of the minimum mean-square error (MMSE) estimator and provides a new lower bound which connects the diff ...

IEEE2021

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Mean percentage error

TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression

Glenohumeral joint force prediction with deep learning

Range-separated hybrid functionals for accurate prediction of band gaps of extended systems

Rapid motion estimation and correction using self-encoded FID navigators in 3D radial MRI

DELMEP: a deep learning algorithm for automated annotation of motor evoked potential latencies

Statistical limits of dictionary learning: Random matrix theory and the spectral replica method

Novel spatiotemporal processing tools for body-surface potential map signals for the prediction of catheter ablation outcome in persistent atrial fibrillation

Worldwide performance evaluation of ground surface reflectance models

Discrete-Choice Mining of Social Processes

Differential Entropy of the Conditional Expectation under Gaussian Noise

Graph Chatbot

Chattez avec Graph Search

TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression

Glenohumeral joint force prediction with deep learning

Rapid motion estimation and correction using self-encoded FID navigators in 3D radial MRI

DELMEP: a deep learning algorithm for automated annotation of motor evoked potential latencies

Differential Entropy of the Conditional Expectation under Gaussian Noise

Novel spatiotemporal processing tools for body-surface potential map signals for the prediction of catheter ablation outcome in persistent atrial fibrillation

Discrete-Choice Mining of Social Processes

Range-separated hybrid functionals for accurate prediction of band gaps of extended systems

Worldwide performance evaluation of ground surface reflectance models

Statistical limits of dictionary learning: Random matrix theory and the spectral replica method