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Found 4 papers, 1 papers with code
FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning
Additionally, we observe that as data heterogeneity increases, the gap between higher feature norms for observed classes, obtained from local models, and feature norms of unobserved classes widens, in contrast to the behavior of classifier weight norms.
Model-Free Reconstruction of Capacity Degradation Trajectory of Lithium-Ion Batteries Using Early Cycle Data
The method involves predicting a few knots at specific retention levels using a deep learning-based model and interpolating them to reconstruct the trajectory.
Mitigating Dataset Bias by Using Per-sample Gradient
In this study, we propose a debiasing algorithm, called PGD (Per-sample Gradient-based Debiasing), that comprises three steps: (1) training a model on uniform batch sampling, (2) setting the importance of each sample in proportion to the norm of the sample gradient, and (3) training the model using importance-batch sampling, whose probability is obtained in step (2).
Impedance-based Capacity Estimation for Lithium-Ion Batteries Using Generative Adversarial Network
This paper proposes a fully unsupervised methodology for the reliable extraction of latent variables representing the characteristics of lithium-ion batteries (LIBs) from electrochemical impedance spectroscopy (EIS) data using information maximizing generative adversarial networks.
