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 ...

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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 ...

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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

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Machine Learning for Optical Motion Capture-Driven Musculoskeletal Modelling from Inertial Motion Capture Data

Fully learnable deep wavelet transform for unsupervised monitoring of high-frequency time series

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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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