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Concept

Probabilité a posteriori

Publications associées (29)

Ergodic Exploration Using Tensor Train: Applications in Insertion Tasks

Sylvain Calinon, Suhan Narayana Shetty

In robotics, ergodic control extends the tracking principle by specifying a probability distribution over an area to cover instead of a trajectory to track. The original problem is formulated as a spectral multiscale coverage problem, typically requiring t ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Discrete-Choice Mining of Social Processes

Victor Kristof

Poor decisions and selfish behaviors give rise to seemingly intractable global problems, such as the lack of transparency in democratic processes, the spread of conspiracy theories, and the rise in greenhouse gas emissions. However, people are more predict ...

EPFL2021

Near-Field Mapping of Photonic Eigenmodes in Patterned Silicon Nanocavities by Electron Energy-Loss Spectroscopy

Duncan Thomas Lindsay Alexander, Valentin Flauraud

Recently, there has been significant interest in using dielectric nanocavities for the controlled scattering of light, owing to the diverse electromagnetic modes that they support. For plasmonic systems, electron energy-loss spectroscopy (EELS) is now an e ...

2021

COVID-19: data-driven dynamic asset allocation in times of pandemic

Anna Timonina-Farkas

The COVID-19 pandemic has demonstrated the importance and value of multi-period asset allocation strategies responding to rapid changes in market behavior. In this article, we formulate and solve a multi-stage stochastic optimization problem, choosing the ...

AMER INST MATHEMATICAL SCIENCES-AIMS2021

A Unified Theory of Robust and Distributionally Robust Optimization via the Primal-Worst-Equals-Dual-Best Principle

Daniel Kuhn, Wolfram Wiesemann, Jianzhe Zhen

Robust and distributionally robust optimization are modeling paradigms for decision-making under uncertainty where the uncertain parameters are only known to reside in an uncertainty set or are governed by any probability distribution from within an ambigu ...

2023

A t-distribution based operator for enhancing out of distribution robustness of neural network classifiers

Philip Neil Garner

Neural Network (NN) classifiers can assign extreme probabilities to samples that have not appeared during training (out-of-distribution samples) resulting in erroneous and unreliable predictions. One of the causes for this unwanted behaviour lies in the us ...

IEEE Institute of Electrical and Electronics Engineers2020

Time-lapse reconstruction of the fracture front from diffracted waves arrivals in laboratory hydraulic fracture experiments

Brice Tanguy Alphonse Lecampion, Dong Liu, Thomas Emmanuel Blum

4-D acoustic imaging via an array of 32 sources/32 receivers is used to monitor hydraulic fracture (HF) propagating in a 250 mm cubic specimen under a true-triaxial state of stress. We present a method based on the arrivals of diffracted waves to reconstru ...

2020

Reduction of scapulohumeral subluxation with posterior augmented glenoid implants in anatomic total shoulder arthroplasty: Short-term 3D comparison between pre- and post-operative CT

Alexandre Terrier, Alain Farron, Patrick Goetti

Background: Failure rates in anatomic total shoulder arthroplasty (aTSA) are higher in case of asymmetric glenoid bone loss secondary to posterior wear, and in persistent static posterior subluxation of the humeral head (PSH). ...

ELSEVIER MASSON, CORP OFF2020

On Vacuous and Non-Vacuous Generalization Bounds for Deep Neural Networks

Konstantinos Pitas

Deep neural networks have been empirically successful in a variety of tasks, however their theoretical understanding is still poor. In particular, modern deep neural networks have many more parameters than training data. Thus, in principle they should over ...

EPFL2020