Publications related to Low-Rank Adaptation for Transformers in Dynamic Hand Gesture Recognition Using High-Density Surface Electromyography

Related publications (20)

An Analysis of Model Robustness across Concurrent Distribution Shifts

Machine learning models, meticulously optimized for source data, often fail to predict target data when faced with distribution shifts (DSs). Previous benchmarking studies, though extensive, have mainly focused on simple DSs. Recognizing that DSs often occ ...

2025

Temporally-Consistent Surface Reconstruction Using Metrically-Consistent Atlases

Pascal Fua, Mathieu Salzmann, Shaifali Parashar, Jan Bednarík

We propose a method for unsupervised reconstruction of a temporally-consistent sequence of surfaces from a sequence of time-evolving point clouds. It yields dense and semantically meaningful correspondences between frames. We represent the reconstructed su ...

2025

Neural decoding and feature selection methods for closed-loop control of avoidance behavior

Mahsa Shoaran, Jinhan Liu

Objective. Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local fi ...

IOP Publishing2024

Going beyond Compositions, DDPMs Can Produce Zero-Shot Interpolations

Volkan Cevher, Grigorios Chrysos, Igor Krawczuk, Justin Samuel Deschenaux

Denoising Diffusion Probabilistic Models (DDPMs) exhibit remarkable capabilities in image generation, with studies suggesting that they can generalize by composing latent factors learned from the training data. In this work, we go further and study DDPMs t ...

2024

Advances in Algorithms for Sampling, Optimization and Inference in Disordered Systems

Giovanni Piccioli

This thesis focuses on the development of advanced algorithmic techniques, primarily Markov Chain Monte Carlo (MCMC) methods, and message passing algorithms, to tackle high-dimensional optimization and inference problems. The algorithms used have a probabi ...

EPFL2024

HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields: Processed data and trained models

Mathieu Salzmann, Alexander Mathis, Chen Zhao, Haozhe Qi

HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024 Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis. Affiliation: EPFL Date: June, 2024 Link to the CVPR article: https://openaccess.thecvf.com/conte ...

Zenodo2024

Multi-Label Generalized Zero Shot Chest X-Ray Classification by Combining Image-Text Information With Feature Disentanglement

Seyedbehzad Bozorgtabar

In fully supervised learning-based medical image classification, the robustness of a trained model depends on its exposure to various disease classes. Generalized Zero Shot Learning (GZSL) aims to predict both seen and novel unseen classes. While most GZSL ...

Institute of Electrical and Electronics Engineers2024

Ground-based image deconvolution with Swin Transformer UNet

Frédéric Courbin, Pascale Jablonka, Utsav Akhaury

Aims. As ground-based all-sky astronomical surveys will gather millions of images in the coming years, a critical requirement emerges for the development of fast deconvolution algorithms capable of efficiently improving the spatial resolution of these imag ...

2024

Changes in spinal motor behaviour are associated with reduction in disability in chronic low back pain: A longitudinal cohort study with 1-year follow-up

Brigitte Jolles-Haeberli

Background: The need to improve spinal motor behaviour in chronic low back pain (CLBP) rehabilitation remains unclear. The objective of this study was to test if changes in spinal motor behaviour were associated with changes in disability after an interdis ...

2024

On the modelling of the fatigue-induced damage in Francis turbines start-up sequences

Elena Vagnoni, Martin Seydoux, Ekaterina Krymova, Alessandro Morabito

The European Green Deal is prescribing a very ambitious rise in the annual production of renewable energy sources, and grid regulation services are increasingly important in the course of a massive integration of intermittent renewable energy sources. Alth ...

IOP Publishing2024

Enhancing spatial resolution in MÖNCH for electron microscopy via deep learning

Benjamin Bejar Haro, Luis Felipe Barba Flores, Carl Pierre Jacques Remlinger

However, their use in imaging applications has been limited by their relatively large pixel size (≥ 55 μm) and high-energy (>80 keV) electrons scattering over multiple pixels in the sensor layer. To realize the full potential of fast, radiation-hard HPDs a ...

2024

The Esntorch Library: Efficient Implementation of Transformer-based Echo State Networks

Mathieu Louis Rochat

Transformer-based models have revolutionized NLP. But in general, these models are highly resource consuming. Based on this consideration, several reservoir computing approaches to NLP have shown promising results. In this context, we propose EsnTorch, a l ...

Springer Nature2023

Learning sparse features can lead to overfitting in neural networks

Matthieu Wyart, Leonardo Petrini, Francesco Cagnetta

It is widely believed that the success of deep networks lies in their ability to learn a meaningful representation of the features of the data. Yet, understanding when and how this feature learning improves performance remains a challenge. For example, it ...

Iop Publishing Ltd2023

Brain networks subserving functional core processes of emotions identified with componential modeling

Dimitri Nestor Alice Van De Ville

Despite a lack of scientific consensus on the definition of emotions, they are generally considered to involve several modifications in the mind, body, and behavior. Although psychology theories emphasized multi-componential characteristics of emotions, li ...

OXFORD UNIV PRESS INC2023

Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors

Matthias Rottmann

The majority of uncertainty quantification methods for deep object detectors are based on the network output, such as sampling strategies like Monte-Carlo dropout or deep ensembles with straight-forward transfers to object detection. Here, we study gradien ...

IEEE Computer Soc2023

GenIE: Generative Information Extraction

Robert West, Maxime Jean Julien Peyrard, Martin Josifoski

Structured and grounded representation of text is typically formalized by closed information extraction, the problem of extracting an exhaustive set of (subject, relation, object) triplets that are consistent with a predefined set of entities and relations ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2022

Spatio-Temporal Graph Neural Networks for Multi-Site PV Power Forecasting

Jelena Simeunovic

Accurate forecasting of solar power generation with fine temporal and spatial resolution is vital for the operation of the power grid. However, state-of-the-art approaches that combine machinelearning with numerical weather predictions (NWP) have coarse re ...

2022

ResLogit: A residual neural network logit model for data-driven choice modelling

Melvin Jian Wen Wong

This paper presents a novel deep learning-based travel behaviour choice model. Our proposed Residual Logit (ResLogit) model formulation seamlessly integrates a Deep Neural Network (DNN) architecture into a multinomial logit model. Recently, DNN models such ...

PERGAMON-ELSEVIER SCIENCE LTD2021

Runtime Replacement of Machine Learning Modules in Fpga-based Systems

Dina Gamaleldin Ahmed Shawky Mahmoud

Field Programmable Gate Arrays (FPGAs) are often used for accelerating machine learning algorithms. These algorithms can be implemented by various architectures which may differ in performance, required area, power consumption and reliability. This paper p ...

IEEE2021

TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive?

Bastien Germain C. Van Delft, Alexandre Massoud Alahi, Yuejiang Liu, Taylor Ferdinand Mordan, Parth Ashit Kothari

Test-time training (TTT) through self-supervised learning (SSL) is an emerging paradigm to tackle distributional shifts. Despite encouraging results, it remains unclear when this approach thrives or fails. In this work, we first provide an in-depth look at ...

2021