Concept
Elastic net regularization
Related publications (144)
Towards Trustworthy Deep Learning for Image Reconstruction
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 ...
EPFL2024Efficient 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 ...
2024High-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 ...
2024Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets
Herein, machine learning (ML) models using multiple linear regression (MLR), support vector regression (SVR), random forest (RF) and artificial neural network (ANN) are developed and compared to predict the output features viz. specific capacitance (Csp), ...
Lausanne2024Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions
Florent Evariste Forest, Yunhong Che
Battery health prediction is significant while challenging for intelligent battery management. This article proposes a general framework for both short-term and long-term predictions of battery health under unseen dynamic loading and temperature conditions ...
Ieee-Inst Electrical Electronics Engineers Inc2024Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks
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 ...
EPFL2024Saddle-to-Saddle Dynamics in Diagonal Linear Networks
Nicolas Henri Bernard Flammarion, Scott William Pesme
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 ...
2023(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability
Nicolas Henri Bernard Flammarion, Scott William Pesme, Mathieu Even
In this paper, we investigate the impact of stochasticity and large stepsizes on the implicit regularisation of gradient descent (GD) and stochastic gradient descent (SGD) over diagonal linear networks. We prove the convergence of GD and SGD with macroscop ...
2023Automatic Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning
We propose two deep learning models that fully automate shape parameterization for aerodynamic shape optimization. Both models are optimized to parameterize via deep geometric learning to embed human prior knowledge into learned geometric patterns, elimina ...
2023Stability of Image-Reconstruction Algorithms
Michaël Unser, Sebastian Jonas Neumayer, Pol del Aguila Pla
Robustness and stability of image-reconstruction algorithms have recently come under scrutiny. Their importance to medical imaging cannot be overstated. We review the known results for the topical variational regularization strategies ( ℓ2 and ℓ1 regulariz ...
2023