Temporally-Consistent Surface Reconstruction Using Metrically-Consistent Atlases

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Simulations of chemical reactivity in condensed phase systems represent an ongoing challenge in computational chemistry, where traditional quantum chemical approaches typically struggle with both the size of the system and the potential complexity of the r ...

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 ...

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 ...

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 ...

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 ...

Persistent Homology (PH) has been successfully used to train networks to detect curvilinear structures and to improve the topological quality of their results. However, existing methods are very global and ignore the location of topological features. In th ...

Temporally-Consistent Surface Reconstruction Using Metrically-Consistent Atlases

Chattez avec Graph Search

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

An Analysis of Model Robustness across Concurrent Distribution Shifts

Learning Algorithms in the Limit

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

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

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

Going beyond Compositions, DDPMs Can Produce Zero-Shot Interpolations

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

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

Enhancing spatial resolution in MÖNCH for electron microscopy via deep learning

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

Ground-based image deconvolution with Swin Transformer UNet

Capturing Dichotomic Solvent Behavior in Solute−Solvent Reactions with Neural Network Potentials

Learning sparse features can lead to overfitting in neural networks

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

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

Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors

Persistent Homology with Improved Locality Information for more Effective Delineation

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