Concept
Mean squared prediction error
Publications associées (295)
Partial discharge localization in power transformer tanks using machine learning methods
Marcos Rubinstein, Hamidreza Karami
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 ...
2024Intermediate complexity atmospheric modeling in complex terrain: is it right?
Michael Lehning, Dylan Stewart Reynolds, Michael Haugeneder
Dynamic downscaling of atmospheric forcing data to the hectometer resolution has shown increases in accuracy for landsurface models, but at great computational cost. Here we present a validation of a novel intermediate complexity atmospheric model, HICAR, ...
Frontiers Media Sa2024Spectral Estimators for High-Dimensional Matrix Inference
A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...
EPFL2024Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions
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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 Inc2024Unveiling the complexity of learning and decision-making
Reinforcement learning (RL) is crucial for learning to adapt to new environments. In RL, the prediction error is an important component that compares the expected and actual rewards. Dopamine plays a critical role in encoding these prediction errors. In my ...
EPFL2024Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks
We study the problem of estimating an unknown function from noisy data using shallow ReLU neural networks. The estimators we study minimize the sum of squared data-fitting errors plus a regularization term proportional to the squared Euclidean norm of the ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023Timing and spatial selection bias in rapid extreme event attribution
Anthony Christopher Davison, Ophélia Mireille Anna Miralles
Selection bias may arise when data have been chosen in a way that subsequent analysis does not account for. Such bias can arise in climate event attribution studies that are performed rapidly after a devastating "trigger event'', whose occurrence correspon ...
ELSEVIER2023Modeling local thermal responses of individuals: Validation of advanced human thermo-physiology models
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Human thermo-physiology models (HTPM) are useful tools to assess dynamic and non-uniform human thermal states. However, they are developed based on the physiological data of an average person. In this paper, we present a detailed evaluation of two sophisti ...
2023State-Based Versus Time-Based Estimation of the Gait Phase for Hip Exoskeletons in Steady and Transient Walking
Auke Ijspeert, Mohamed Bouri, Ali Reza Manzoori, Tian Ye
The growing demand for online gait phase (GP) estimation, driven by advancements in exoskeletons and prostheses, has prompted numerous approaches in the literature. Some approaches explicitly use time, while others rely on state variables to estimate the G ...
IEEE 2023Quantization for Decentralized Learning Under Subspace Constraints
Ali H. Sayed, Stefan Vlaski, Roula Nassif, Marco Carpentiero
In this article, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces. This constrained fo ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023