Publications associées à Is there a common factor for vision?

Weak correlations between visual abilities in healthy older adults, despite long-term performance stability

Using batteries of visual tests, most studies have found that there are only weak correlations between the performance levels of the tests. Factor analysis has confirmed these results. This means that a participant excelling in one test may rank low in ano ...

2024

Robust machine learning for neuroscientific inference

Steffen Schneider

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

EPFL2024

Transportation-based functional ANOVA and PCA for covariance operators

Victor Panaretos, Yoav Zemel, Valentina Masarotto

We consider the problem of comparing several samples of stochastic processes with respect to their second-order structure, and describing the main modes of variation in this second order structure, if present. These tasks can be seen as an Analysis of Vari ...

Inst Mathematical Statistics-Ims2024

Scalable and Privacy-Preserving Federated Principal Component Analysis

Jean-Pierre Hubaux, Jean-Philippe Léonard Bossuat, David Jules Froelicher, Joao André Gomes de Sá e Sousa, Juan Ramón Troncoso-Pastoriza, Apostolos Pyrgelis

Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on private data distributed among multiple data providers while ensuring data confi ...

IEEE COMPUTER SOC2023

Quantum mixed state compiling

Zoë Holmes

The task of learning a quantum circuit to prepare a given mixed state is a fundamental quantum subroutine. We present a variational quantum algorithm (VQA) to learn mixed states which is suitable for near-term hardware. Our algorithm represents a generaliz ...

IOP Publishing Ltd2023

From Undercomplete to Sparse Overcomplete Autoencoders to Improve LF-MMI Speech Recognition

Hervé Bourlard, Selen Hande Kabil

Starting from a strong Lattice-Free Maximum Mutual Information (LF-MMI) baseline system, we explore different autoencoder configurations to enhance Mel-Frequency Cepstral Coefficients (MFCC) features. Autoencoders are expected to generate new MFCC features ...

ISCA-INT SPEECH COMMUNICATION ASSOC2022

Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs

Tanja Christina Käser Jacober, Natasa Krco, Vinitra Swamy, Mirko Marras, Bahar Radmehr

Neural networks are ubiquitous in applied machine learning for education. Their pervasive success in predictive performance comes alongside a severe weakness, the lack of explainability of their decisions, especially relevant in human-centric fields. We im ...

2022

MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square

Yue Pan, Yujie He

The rapid development of autonomous driving and mobile mapping calls for off-the-shelf LiDAR SLAM solutions that are adaptive to LiDARs of different specifications on various complex scenarios. To this end, we propose MULLS, an efficient, low-drift, and ve ...

IEEE2021

Inkjet-printed polymer composites for the detection of volatile organic compounds

Mohammadmahdi Kiaee

Sensors capable of detecting and classifying volatile organic compounds (VOC) have been gaining more attention by the advent of internet-of-things (IoT) enabled devices and integration of various sensing elements into hand-held and portable devices. The re ...

EPFL2021

Effects of eight neuropsychiatric copy number variants on human brain structure

Claudia Modenato, Kuldeep Kumar, Bogdan Draganski, Lester Melie Garcia, Borja Rodriguez Herreros

Many copy number variants (CNVs) confer risk for the same range of neurodevelopmental symptoms and psychiatric conditions including autism and schizophrenia. Yet, to date neuroimaging studies have typically been carried out one mutation at a time, showing ...

SPRINGERNATURE2021

Principal Component Analysis By Optimization Of Symmetric Functions Has No Spurious Local Optima

Armin Eftekhari

Principal component analysis (PCA) finds the best linear representation of data and is an indispensable tool in many learning and inference tasks. Classically, principal components of a dataset are interpreted as the directions that preserve most of its "e ...

SIAM PUBLICATIONS2020

Structure-property maps with Kernel principal covariates regression

Michele Ceriotti, Benjamin Aaron Helfrecht, Guillaume André Jean Fraux

Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to the automatic processing of large amounts of data for building supervised or unsupervised machine learning models. Principal covariates regression (PC ...

2020

Dynamic mode decomposition of resting-state and task fMRI

Dimitri Nestor Alice Van De Ville, Raphaël Pierre Liégeois

Component analysis is a powerful tool to identify dominant patterns of interactions in multivariate datasets. In the context of fMRI data, methods such as principal component analysis or independent component analysis have been used to identify the brain n ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2019

A visual working memory dataset collection with bootstrap Independent Component Analysis for comparison of electroencephalographic preprocessing pipelines

Fiorenzo Artoni

Here we present an electroencephalographic (EEG) collection of 71-channel datasets recorded from 14 subjects (7 males, 7 females, aged 20–40 years) while performing a visual working memory task with a T set of 150 Independent Component Analysis (ICA) decom ...

2019

Time-Frequency Characterization of Resting-State Brain Function Reveals Overlapping Components with Specific Topology and Frequency Content

Dimitri Nestor Alice Van De Ville, Thomas William Arthur Bolton

Even at rest, functional magnetic resonance imaging (fMRI) data displays exquisitely complex temporal dynamics. Here, we deployed a time-frequency analysis to track the modulus of fMRI signals over time, across space (a set of 341 brain areas) and frequenc ...

ASSOC COMPUTING MACHINERY2019

Sparse and Low-rank Modeling for Automatic Speech Recognition

Pranay Dighe

This thesis deals with exploiting the low-dimensional multi-subspace structure of speech towards the goal of improving acoustic modeling for automatic speech recognition (ASR). Leveraging the parsimonious hierarchical nature of speech, we hypothesize that ...

EPFL2019

Improvements on the uniformity of large-area microlens arrays in Fused Silica

Wilfried Noell, Toralf Scharf, Jeremy Béguelin, Raoul Kirner

The uniformity of large microlens arrays in Fused Silica is governed by the production process. It comprises photolithographic patterning of a spin-coated layer of photoresist on a 200mm wafer with a molten resist reflow process and subsequent dry etching. ...

OPTICAL SOC AMER2019

Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition

Fiorenzo Artoni

Independent Component Analysis (ICA) has proven to be an effective data driven method for analyzing EEG data, separating signals from temporally and functionally independent brain and non-brain source processes and thereby increasing their definition. Dime ...

2018

PCA and deep learning based myoelectric grasping control of a prosthetic hand

Huaiqi Huang

Background: For the functional control of prosthetic hand, it is insufficient to obtain only the motion pattern information. As far as practicality is concerned, the control of the prosthetic hand force is indispensable. The application value of prosthetic ...

2018

Dictionary Learning Based on Sparse Distribution Tomography

Michaël Unser, Pedram Pad

We propose a new statistical dictionary learning algorithm for sparse signals that is based on an α-stable innovation model. The parameters of the underlying model—that is, the atoms of the dictionary, the sparsity index α and the dispersion of the transfo ...

P. Pad2017