Publications associées à Machine-Learning-Powered Neural Interfaces for Smart Prosthetics and Diagnostics

Publications associées (22)

Predicting Attention Deficits and Functional Recovery After Glioma Resection Through Functional Executive Networks: Insights From Dynamic Properties

BackgroundPostoperative short-term attentional and executive dysfunctions are common after brain tumor resection, significantly impacting patients' quality of life and functional recovery. This longitudinal cross-sectional study investigated whether presur ...

SPRINGER2025

Proteome-wide prediction of the mode of inheritance and molecular mechanisms underlying genetic diseases using structural interactomics

Jacques Fellay

Genetic diseases can be classified according to their modes of inheritance and their underlying molecular mechanisms. Autosomal dominant disorders often result from DNA variants that cause loss-of-function, gain-of-function, or dominant-negative effects, w ...

Elsevier BV2025

From Forest to Zoo: Great Ape Behavior Recognition with Chimpbehave

Jean-Marc Odobez

This paper addresses the significant challenge of recognizing behaviors in non-human primates, specifically focusing on chimpanzees. Automated behavior recognition is crucial for both conservation efforts and the advancement of behavioral research. However ...

SPRINGER2025

Modelling cochlea and its interaction with the auditory path for speech processing

Louise Clothilde Coppieters De Gibson

This thesis explores the intersection of physiological modelling and computational techniques in advancing Automatic Speech Recognition (ASR) systems. Contemporary ASR, often driven by attention models and self-supervised learning, has achieved remarkable ...

EPFL2025

Decoding the brain: From neural representations to mechanistic models

Alexander Mathis, Mackenzie Mathis, Adriana Perez Rotondo

A central principle in neuroscience is that neurons within the brain act in concert to produce perception, cognition, and adaptive behavior. Neurons are organized into specialized brain areas, dedicated to different functions to varying extents, and their ...

CELL PRESS2024

Measurement - Based/Model-Less Estimation of Voltage Sensitivity Coefficients by Feedforward and LSTM Neural Networks in Power Distribution Grids

Rahul Kumar Gupta

The increasing adoption of measurement units in electrical power distribution grids has enabled the deployment of data-driven and measurement-based control schemes. Such schemes rely on measurement-based estimated models, where the models are first estimat ...

Institute of Electrical and Electronics Engineers Inc.2024

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (4/6)

Jean-Philippe Thiran, Roser Vinals Terres

This dataset contains a collection of ultrafast ultrasound acquisitions from nine volunteers and the CIRS 054G phantom. For a comprehensive understanding of the dataset, please refer to the paper: Viñals, R.; Thiran, J.-P. A KL Divergence-Based Loss for In ...

EPFL Infoscience2024

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Jean-Paul Richard Kneib, Emma Elizabeth Tolley, Michele Bianco, Tianyue Chen

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Oxford Univ Press2024

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Amirhossein Shahbazinia, David Atienza Alonso, Giovanni Ansaloni, Flavio Ponzina, José Angel Miranda Calero, Jonathan Dan

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

IEEE2024

MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning

Alexandre Massoud Alahi, Mohamed Ossama Ahmed Abdelfattah, Mariam Ahmed Mahmoud Hegazy Hassan

Current transformer-based skeletal action recognition models tend to focus on a limited set of joints and low-level motion patterns to predict action classes. This results in significant performance degradation under small skeleton perturbations or changin ...

2024

Optical data processing using structural non-linearity

Demetri Psaltis, Christophe Moser, Niyazi Ulas Dinç, Ilker Oguz, Mustafa Yildirim

A device (1) and method for processing data comprises at least one input layer (2), at least one modulation layer (3), and at least one output layer (4). The input layer (2) is configured to receive at least one carrier signal (Sc). The modulation layer (3 ...

2024

A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods

Michaël Unser, Pakshal Narendra Bohra, Pol del Aguila Pla

We present a statistical framework to benchmark the performance of reconstruction algorithms for linear inverse problems, in particular, neural-network-based methods that require large quantities of training data. We generate synthetic signals as realizati ...

2023

Deep learning predicts malignancy and metastasis of solid pulmonary nodules from CT scans

Jiancheng Yang

In the clinic, it is difficult to distinguish the malignancy and aggressiveness of solid pulmonary nodules (PNs). Incorrect assessments may lead to delayed diagnosis and an increased risk of complications. We developed and validated a deep learning-based m ...

FRONTIERS MEDIA SA2023

Robust Collaborative Learning with Linear Gradient Overhead

Rachid Guerraoui, Nirupam Gupta, Rafaël Benjamin Pinot, Le Nguyen Hoang, Sadegh Farhadkhani, John Stephan

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been ...

PLMR2023

Complex Representation Learning with Graph Convolutional Networks for Knowledge Graph Alignment

Quoc Viet Hung Nguyen, Thành Tâm Nguyên, Thanh Trung Huynh

The task of discovering equivalent entities in knowledge graphs (KGs), so-called KG entity alignment, has drawn much attention to overcome the incompleteness problem of KGs. The majority of existing techniques learns the pointwise representations of entiti ...

Wiley-Hindawi2023

Meteor: Meta-learning connecting earth problems observed from space

Devis Tuia, Marc Conrad Russwurm, Benjamin Alexander Kellenberger

Satellite remote sensing has become a key technology for monitoring Earth and the processes occurring at its surface. It relies on state-of-the-art machine learning models that require large annotated datasets to capture the extreme diversity of the proble ...

2023

Locality defeats the curse of dimensionality in convolutional teacher-student scenarios*

Matthieu Wyart, Alessandro Favero, Francesco Cagnetta

Convolutional neural networks perform a local and translationally-invariant treatment of the data: quantifying which of these two aspects is central to their success remains a challenge. We study this problem within a teacher-student framework for kernel r ...

IOP Publishing Ltd2022

Democratizing Machine Learning

Nirupam Gupta, Rafaël Benjamin Pinot, Alexandre David Olivier Maurer

The increasing prevalence of personal devices motivates the design of algorithms that can leverage their computing power, together with the data they generate, in order to build privacy-preserving and effective machine learning models. However, traditional ...

IEEE2022

Manifold Learning-Based Polynomial Chaos Expansions For High-Dimensional Surrogate Models

Ketson Roberto Maximiano Dos Santos

In this work we introduce a manifold learning-based method for uncertainty quantification (UQ) in systems describing complex spatiotemporal processes. Our first objective is to identify the embedding of a set of high-dimensional data representing quantitie ...

2022

Bayesian Recurrent Units and the Forward-Backward Algorithm

Philip Neil Garner

Using Bayes's theorem, we derive a unit-wise recurrence as well as a backward recursion similar to the forward-backward algorithm. The resulting Bayesian recurrent units can be integrated as recurrent neural networks within deep learning frameworks, while ...

ISCA-INT SPEECH COMMUNICATION ASSOC2022