Supervised learning

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Related publications (31)

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Marco Picasso, Maude Girardin, Alexandre Caboussat

We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

Elsevier Science Sa2024

SELF-SUPERVISED ISOTROPIC SUPERRESOLUTION FETAL BRAIN MRI

Michaël Unser, Meritxell Bach Cuadra

Superresolution T2-weighted fetal-brain magnetic-resonance imaging (FBMRI) traditionally relies on the availability of several orthogonal low-resolution series of 2-dimensional thick slices (volumes). In practice, only a few low-resolution volumes are acqu ...

IEEE2023

Learning ground states of gapped quantum Hamiltonians with Kernel Methods

Giuseppe Carleo, Riccardo Rossi, Clemens Giuliani, Filippo Vicentini

Neural network approaches to approximate the ground state of quantum hamiltonians require the numerical solution of a highly nonlinear optimization problem. We introduce a statistical learning approach that makes the optimization trivial by using kernel me ...

Verein Forderung Open Access Publizierens Quantenwissenschaf2023

A semi-supervised algorithm for improving the consistency of crowdsourced datasets: The COVID-19 case study on respiratory disorder classification

David Atienza Alonso, Tomas Teijeiro Campo, Lara Orlandic

Background and Objective: Cough audio signal classification is a potentially useful tool in screening for respiratory disorders, such as COVID-19. Since it is dangerous to collect data from patients with contagious diseases, many research teams have turned ...

2023

A structured prediction approach for robot imitation learning

Aude Billard, Iason Batzianoulis, Anqing Duan

We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observed to have a prominent role. Structured prediction is a form of supervised le ...

Sage Publications Ltd2023

Byzantine Machine Learning: A Primer

Rachid Guerraoui, Nirupam Gupta, Rafaël Benjamin Pinot

The problem of Byzantine resilience in distributed machine learning, a.k.a., Byzantine machine learning, consists in designing distributed algorithms that can train an accurate model despite the presence of Byzantine nodes, i.e., nodes with corrupt data or ...

2023

Knowledge Distillation for 6D Pose Estimation by Aligning Distributions of Local Predictions

Mathieu Salzmann, Shuxuan Guo, Yinlin Hu

Knowledge distillation facilitates the training of a compact student network by using a deep teacher one. While this has achieved great success in many tasks, it remains completely unstudied for image-based 6D object pose estimation. In this work, we intro ...

Ieee Computer Soc2023

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Rachid Guerraoui, John Stephan, Youssef Allouah, Nirupam Gupta, Rafaël Benjamin Pinot

The ubiquity of distributed machine learning (ML) in sensitive public domain applications calls for algorithms that protect data privacy, while being robust to faults and adversarial behaviors. Although privacy and robustness have been extensively studied ...

2023

Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity

Rachid Guerraoui, John Stephan, Sadegh Farhadkhani, Youssef Allouah, Nirupam Gupta, Rafaël Benjamin Pinot

Byzantine machine learning (ML) aims to ensure the resilience of distributed learning algorithms to misbehaving (or Byzantine) machines. Although this problem received significant attention, prior works often assume the data held by the machines to be homo ...

PMLR2023

Distributed Optimization with Byzantine Robustness Guarantees

Lie He

As modern machine learning continues to achieve unprecedented benchmarks, the resource demands to train these advanced models grow drastically. This has led to a paradigm shift towards distributed training. However, the presence of adversariesâ whether ma ...

EPFL2023

From Kernel Methods to Neural Networks: A Unifying Variational Formulation

Michaël Unser

The minimization of a data-fidelity term and an additive regularization functional gives rise to a powerful framework for supervised learning. In this paper, we present a unifying regularization functional that depends on an operator L\documentclass[12pt]{ ...

Springer2023

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