Ridges, Neural Networks, and the Radon Transform

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Training high-quality deep learning models is a challenging task due to computational and technical requirements. A growing number of individuals, institutions, and companies increasingly rely on pre-trained, third-party models made available in public rep ...

Elucidating the functional effects of missense variants is crucial yet challenging. To investigate their impact, we fine-tuned protein language models, including ESM2 and ProtT5, to classify 20 protein features at amino acid resolution. In addition, we tra ...

Accurate human trajectory prediction is one of the most crucial tasks for autonomous driving, ensuring its safety. Yet, existing models often fail to fully leverage the visual cues that humans subconsciously communicate when navigating the space. In this w ...

Phase retrieval, a nonlinear problem prevalent in imaging applications, has been extensively studied using random models, some of which with i.i.d. sensing matrix components. While these models offer robust reconstruction guarantees, they are computational ...

The Songdo Traffic dataset delivers precisely georeferenced vehicle trajectories captured through high-altitude bird's-eye view (BeV) drone footage over Songdo International Business District, South Korea. Comprising approximately 700,000 unique trajectori ...

We consider a family of boundary integral operators supported on a collection of parametrically defined bounded Lipschitz boundaries. Consequently, the boundary integral operators themselves also depend on the parametric variables, thus leading to a parame ...

Quantifying the uncertainty of regression models is essential to ensure their reliability, particularly since their application often extends beyond their training domain. Based on the solution of a constrained optimization problem, this work proposes ‘pre ...

Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes ...

© The Author(s) 2024.Deep learning models are widely used for traffic forecasting on freeways due to their ability to learn complex temporal and spatial relationships. In particular, graph neural networks, which integrate graph theory into deep learning, h ...

Marker-based Optical Motion Capture (OMC) systems and associated musculoskeletal (MSK) modelling predictions offer non-invasively obtainable insights into muscle and joint loading at an in vivo level, aiding clinical decision-making. However, an OMC system ...

High-frequency (HF) signals are ubiquitous in the industrial world and are of great use for monitoring of industrial assets. Most deep-learning tools are designed for inputs of fixed and/or very limited size and many successful applications of deep learnin ...

Irregular sampling of time series in electronic health records (EHRs) presents a challenge for the development of machine learning models. Additionally, the pattern of missing data in certain clinical variables is not random, but depends on the decisions o ...

Machine learning (ML) techniques applied to quantum many-body physics have emerged as a new research field. While the numerical power of this approach is undeniable, the most expressive ML algorithms, such as neural networks, are black boxes: The user does ...

This paper studies the continuous-domain inverse problem of recovering Radon measures on the one-dimensional torus from low-frequency Fourier coefficients, where K (c) is the cutoff frequency. Our approach consists in minimizing the total-variation norm am ...

Segmenting histology images into diagnostically relevant regions is imperative to support timely and reliable decisions by pathologists. To this end, computer-aided techniques have been proposed to delineate relevant regions in scanned histology slides. Ho ...

Active learning (AL) is a branch of machine learning that deals with problems where unlabeled data is abundant yet obtaining labels is expensive. The learning algorithm has the possibility of querying a limited number of samples to obtain the corresponding ...

Ridges, Neural Networks, and the Radon Transform

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Fine-tuning protein language models to understand the functional impact of missense variants

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Structured Random Model for Fast and Robust Phase Retrieval

Songdo Traffic: High Accuracy Georeferenced Vehicle Trajectories from a Large-Scale Study in a Smart City

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A prediction rigidity formalism for low-cost uncertainties in trained neural networks

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Hessian-based toolbox for reliable and interpretable machine learning in physics

On the uniqueness of solutions for the basis pursuit in the continuum

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