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

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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 ...

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 ...

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 ...

Collaborative Filtering (CF) is a crucial task in recommendation systems, aimed at predicting user preferences based on the behaviors and preferences of similar users. While traditional CF techniques have achieved significant success by learning low-dimens ...

The dynamic aperture is an essential concept in circular particle accelerators, providing the extent of the phase space region where particle motion remains stable over multiple turns. The accurate prediction of the dynamic aperture is key to optimising pe ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

As educational technology becomes increasingly integrated into curricula, more students are engaging with online learning platforms, interactive simulations, and MOOCs. These unstructured environments record students’ behaviors, providing a rich source of ...

Legged robots are becoming increasingly agile in exhibiting dynamic behaviors such as running and jumping. Usually, such behaviors are either optimized and engineered offline (i.e. the behavior is designed for before it is needed), either through model-bas ...

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 ...

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 ...

The objective of this paper is to investigate a new numerical method for the approximation of the self-diffusion matrix of a tagged particle process defined on a grid. While standard numerical methods make use of long-time averages of empirical means of de ...

We present a framework for performing regression when both covariate and response are probability distributions on a compact and convex subset of

\R^d

. Our regression model is based on the theory of optimal transport and links the conditional Fr'echet m ...

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 ...

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 ...

In this paper we fully describe the trajectory of gradient flow over diagonal linear networks in the limit of vanishing initialisation. We show that the limiting flow successively jumps from a saddle of the training loss to another until reaching the minim ...

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

Chattez avec Graph Search

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

From Forest to Zoo: Great Ape Behavior Recognition with Chimpbehave

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

High-Order Recommendation with Heterophilic Hypergraph Diffusion

Machine Learning Techniques for Uncertainty Estimation in Dynamic Aperture Prediction

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

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

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

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

Decoding the brain: From neural representations to mechanistic models

Optical data processing using structural non-linearity

An Interpretable Approach to Identify Performance Indicators Within Unstructured Learning Environments

Quadruped-Frog: Rapid Online Optimization of Continuous Quadruped Jumping

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

Robust Collaborative Learning with Linear Gradient Overhead

Tensor approximation of the self-diffusion matrix of tagged particle processes

Distributional Regression and Autoregression via Optimal Transport

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

Complex Representation Learning with Graph Convolutional Networks for Knowledge Graph Alignment

Saddle-to-Saddle Dynamics in Diagonal Linear Networks