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20022025
most citedPractical Bayesian Optimization of Machine Learning Algorithms

5.7k citations

Showing 2021Show all

21 papers · 1 filter

eess.IV20212 cited

Echocardiography Segmentation with Enforced Temporal Consistency

Nathan Painchaud, Nicolas Duchateau, Olivier Bernard +1

Convolutional neural networks (CNN) have demonstrated their ability to segment 2D cardiac ultrasound images. However, despite recent successes according to which the intra-observer…

stat.ML202123 cited

The R package sentometrics to compute, aggregate and predict with textual sentiment

David Ardia, Keven Bluteau, Samuel Borms +1

We provide a hands-on introduction to optimized textual sentiment indexation using the R package sentometrics. Textual sentiment analysis is increasingly used to unlock the potenti…

cond-mat.str-el20218 cited

Weyl Nodal-Ring Semimetallic Behavior and Topological Superconductivity in Crystalline Forms of Su-Schrieffer-Heeger Chains

Peter Rosenberg, Efstratios Manousakis

We consider a three-dimensional model of coupled Su-Schrieffer-Heeger (SSH) chains. The analytically soluble model discussed here reliably reproduces the features of the band struc…

cond-mat.str-el20212 cited

Nonadiabatic Fluctuations and the Charge-Density-Wave Transition in One-Dimensional Electron-Phonon Systems: a Dynamic Self-Consistent

Alain M. Dikande, C. Bourbonnais

The Peierls instability in one-dimensional electron-phonon systems is known to be qualitatively well described by the Mean-Field theory, however the related self-consistent problem…

cond-mat.mes-hall20212 cited

Quantum magnetic oscillations in Weyl semimetals with tilted nodes

Samuel Vadnais, Rene Cote

A Weyl semimetal (WSM)\ is a three-dimensional topological phase of matter where pairs of nondegenerate bands cross at isolated points in the Brillouin zone called Weyl nodes. Near…

physics.app-ph2021

Multi-terminal memristive devices enabling tunable synaptic plasticity in neuromorphic hardware: a mini-review

Yann Beilliard, Fabien Alibart

Neuromorphic computing based on spiking neural networks has the potential to significantly improve on-line learning capabilities and energy efficiency of artificial intelligence, s…