Publications associées à Geodesic Distance on Optimally Regularized Functional Connectomes Uncovers Individual Fingerprints

High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

This paper studies kernel ridge regression in high dimensions under covariate shifts and analyzes the role of importance re-weighting. We first derive the asymptotic expansion of high dimensional kernels under covariate shifts. By a bias-variance decomposi ...

2024

Efficient local linearity regularization to overcome catastrophic overfitting

Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Elias Abad Rocamora

Catastrophic overfitting (CO) in single-step adversarial training (AT) results in abrupt drops in the adversarial test accuracy (even down to 0%). For models trained with multi-step AT, it has been observed that the loss function behaves locally linearly w ...

2024

Towards Trustworthy Deep Learning for Image Reconstruction

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The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

EPFL2024

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This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

Nature Publishing Group2024

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Maksym Andriushchenko

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

EPFL2024

The hindbrain and cortico-reticular pathway in adolescent idiopathic scoliosis

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W B Saunders Co Ltd2024

Weak correlations between visual abilities in healthy older adults, despite long-term performance stability

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Using batteries of visual tests, most studies have found that there are only weak correlations between the performance levels of the tests. Factor analysis has confirmed these results. This means that a participant excelling in one test may rank low in ano ...

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Quantitative T2 Mapping of Acute Pancreatitis

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Background: Quantification of the T2 signal by means of T2 mapping in acute pancreatitis (AP) has the potential to quantify the parenchymal edema. Quantitative T2 mapping may overcome the limitations of previously reported scoring systems for reliable asse ...

Wiley2024

Novel theory and potential applications of central diastolic pressure decay time constant

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Central aortic diastolic pressure decay time constant ( ) is according to the two-element Windkessel model equal to the product of total peripheral resistance (R) times total arterial compliance (C ). As such, it is related to arterial stiffness, which has ...

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Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

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In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

EPFL2024

SPEEDING UP KRYLOV SUBSPACE METHODS FOR COMPUTING f(A)b VIA RANDOMIZATION

Daniel Kressner, Alice Cortinovis

This work is concerned with the computation of the action of a matrix function f(A), such as the matrix exponential or the matrix square root, on a vector b. For a general matrix A, this can be done by computing the compression of A onto a suitable Krylov ...

Siam Publications2024

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

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In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

EPFL2024

Incorporating Projective Geometry into Deep Learning

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In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

EPFL2024

Saliency prediction in 360° architectural scenes: Performance and impact of daylight variations

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Saliency models are image-based prediction models that estimate human visual attention. Such models, when applied to architectural spaces, could pave the way for design decisions where visual attention is taken into account. In this study, we tested the pe ...

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Bayes-optimal Learning of Deep Random Networks of Extensive-width

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We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

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Tangent functional connectomes uncover more unique phenotypic traits

Mingkui Wang, Enrico Amico

Functional connectomes (FCs) containing pairwise estimations of functional couplings between pairs of brain regions are commonly represented by correlation matrices. As symmetric positive definite matrices, FCs can be transformed via tangent space projecti ...

CELL PRESS2023

Regularization of polynomial networks for image recognition

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Deep Neural Networks (DNNs) have obtained impressive performance across tasks, however they still remain as black boxes, e.g., hard to theoretically analyze. At the same time, Polynomial Networks (PNs) have emerged as an alternative method with a promising ...

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Towards a multiscale point cloud structural similarity metric

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Point clouds are effective data structures for the rep- resentation of three-dimensional media and hence adopted in a wide range of practical applications. In many cases, the portrayed data is expected to be visualized by humans. After acquisition, point c ...

IEEE2023

Regularization of polynomial networks for image recognition

Volkan Cevher, Bohan Wang, Grigorios Chrysos

Deep Neural Networks (DNNs) have obtained impressive performance across tasks, however they still remain as black boxes, e.g., hard to theoretically analyze. At the same time, Polynomial Networks (PNs) have emerged as an alternative method with a promising ...

Ieee Computer Soc2023

Prediction of post-stroke motor recovery benefits from measures of sub-acute widespread network damages

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Rivier et al. report that global measures of brain connectivity estimated by introducing virtual lesions in healthy structural connectomes significantly increase the accuracy of motor recovery prediction in stroke patients when added to focal and clinical ...

OXFORD UNIV PRESS2023