Publications associées à Almost exact recovery in noisy semi-supervised learning

Publications associées (11)

Robust Confinement State Classification with Uncertainty Quantification Through Ensembled Data-driven Methods

Olivier Sauter, Alessandro Pau, Yoeri Renier Johannes Poels, Cristina Venturini

Maximizing fusion performance in tokamaks relies on high energy confinement, often achieved through distinct operating regimes. The automated labeling of these confinement states is crucial to enable large-scale analyses or for real-time control applicatio ...

IOP Publishing Ltd2025

On fundamental aspects of quantum extreme learning machines

Weijie Xiong, Mehrad Sahebi, Giorgio Facelli, Zoë Holmes, Thanasilp Supanut

Quantum extreme learning machines (QELMs) have emerged as a promising framework for quantum machine learning. Their appeal lies in the rich feature map induced by the dynamics of a quantum substrate-the quantum reservoir-and the efficient post-measurement ...

SPRINGERNATURE2025

From STEM-EDXS data to phase separation and quantification using physics-guided NMF

Thomas Tim Holvoet, Cécile Hébert, Duncan Thomas Lindsay Alexander, Nathanaël Perraudin, Guillaume Romain Obozinski

We present the development of a new algorithm which combines state-of-the-art energy-dispersive x-ray (EDX) spectroscopy theory and a suitable machine learning formulation for the hyperspectral unmixing of scanning transmission electron microscope EDX spec ...

2024

A Joint Graph Signal and Laplacian Denoising Network

Zepeng Zhang

Graph neural network (GNN) models have presented astonishing achievements in various application fields. However, they are shown to be vulnerable to adversarial attacks on graph structure and unnoticeable perturbations on the graph structure can cause sign ...

Institute of Electrical and Electronics Engineers Inc.2024

Stochastic Approximation of Variational Quantum Imaginary Time Evolution

Giuseppe Carleo

The imaginary-time evolution of quantum states is integral to various fields, ranging from natural sciences to classical optimization or machine learning. Since simulating quantum imaginary-time evolution generally requires storing an exponentially large w ...

Institute of Electrical and Electronics Engineers Inc.2023

AmadeusGPT: a natural language interface for interactive animal behavioral analysis

Alexander Mathis, Mackenzie Mathis, Shaokai Ye, Jessy Lauer, Mu Zhou

The process of quantifying and analyzing animal behavior involves translating the naturally occurring descriptive language of their actions into machine-readable code. Yet, codifying behavior analysis is often challenging without deep understanding of anim ...

2023

Forward-forward training of an optical neural network

Demetri Psaltis, Christophe Moser, Niyazi Ulas Dinç, Ilker Oguz, Mustafa Yildirim, Jih-Liang Hsieh

Neural networks (NNs) have demonstrated remarkable capabilities in various tasks, but their computation-intensive nature demands faster and more energy-efficient hardware implementations. Optics-based platforms, using technologies such as silicon photonics ...

Optica Publishing Group2023

High-speed identification of suspended carbon nanotubes using Raman spectroscopy and deep learning

Martin Jaggi, Luis Felipe Barba Flores

The identification of nanomaterials with the properties required for energy-efficient electronic systems is usually a tedious human task. A workflow to rapidly localize and characterize nanomaterials at the various stages of their integration into large-sc ...

SPRINGERNATURE2022

Learning curves of generic features maps for realistic datasets with a teacher-student model

Lenka Zdeborová, Florent Gérard Krzakala, Bruno Loureiro, Hugo Chao Cui

Teacher-student models provide a framework in which the typical-case performance of high-dimensional supervised learning can be described in closed form. The assumptions of Gaussian i.i.d. input data underlying the canonical teacher-student model may, howe ...

2021

Model-Agnostic Learning to Meta-Learn

Yatin Dandi, Arnout Jan J Devos

In this paper, we propose a learning algorithm that enables a model to quickly exploit commonalities among related tasks from an unseen task distribution, before quickly adapting to specific tasks from that same distribution. We investigate how learning wi ...

PMLR2021

Work-in-Progress: A Machine Learning-Based Approach for Power and Thermal Management of Next-Generation Video Coding on MPSoCs

David Atienza Alonso, Marina Zapater Sancho, Arman Iranfar

2017