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cs.LG2025
SALVE: Sparse Autoencoder-Latent Vector Editing for Mechanistic Control of Neural Networks
Vegard Flovik
Deep neural networks achieve impressive performance but remain difficult to interpret and control. We present SALVE (Sparse Autoencoder-Latent Vector Editing), a unified "discover,…
cs.LG2025
Gaussian Process Surrogate Models for Efficient Estimation of Structural Response Distributions and Order Statistics
Vegard Flovik, Sebastian Winter, Christian Agrell
Engineering disciplines often rely on extensive simulations to ensure that structures are designed to withstand harsh conditions while avoiding over-engineering for unlikely scenar…
cs.LG2024
Quantifying Distribution Shifts and Uncertainties for Enhanced Model Robustness in Machine Learning Applications
Vegard Flovik
Distribution shifts, where statistical properties differ between training and test datasets, present a significant challenge in real-world machine learning applications where they…