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

Publications associées (20)

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

Dynamic hand gesture recognition (HGR) is critical for real-time prosthetics and neural robotics applications, where high-density surface electromyography (HD-sEMG) signals provide valuable insights into muscle activity. However, adapting machine learning ...

IEEE2025

Learning Algorithms in the Limit

Nicolas Henri Bernard Flammarion, Hristo Georgiev Papazov

This paper studies the problem of learning computable functions in the limit by extending Gold's inductive inference framework to incorporate computational observations and restricted input sources. Complimentary to the traditional Input-Output Observation ...

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

An Analysis of Model Robustness across Concurrent Distribution Shifts

Myeongho Jeon, Shuqing Teresa Yeo

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

Fast-adaptive super-resolution lattice light-sheet microscopy for rapid, long-term, near-isotropic subcellular imaging

Chong Liu

Lattice light-sheet microscopy provides a crucial observation window into intra- and intercellular physiology of living specimens but at the diffraction-limited resolution or anisotropic super-resolution with structured illumination. Here we present meta-l ...

Nature Publishing Group2025

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

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

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

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

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

Nissl_1, Raw images for Machine learning for histological annotation and quantification of cortical layers

Julie Meystre

This dataset contains images (TIFF image data) of brain juvenile rats Wistar Han (P14) scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F5 ...

Zenodo2024

Optimal performance of Graph Convolutional Networks on the Contextual Stochastic Block Model

Patrick Thiran, Guillaume Cyprien Dalle

For Graph Neural Networks, oversmoothing denotes the homogenization of vertex embeddings as the number of layers increases. To better understand this phenomenon, we study community detection with a linearized Graph Convolutional Network on the Contextual S ...

2024

Unlocking Artificial Intelligence Adoption in Local Governments: Best Practice Lessons from Real-World Implementations

Simon Elias Bibri

In an era marked by rapid technological progress, the pivotal role of Artificial Intelligence (AI) is increasingly evident across various sectors, including local governments. These governmental bodies are progressively leveraging AI technologies to enhanc ...

MDPI2024

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

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

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

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

Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: Simulations and experiments

Melanie Nicole Zeilinger, Loris Di Natale

2022