2 papers
cs.LG2026
Efficient Bayesian Updates for Deep Active Learning via Laplace Approximations
Denis Huseljic, Marek Herde, Lukas Rauch +5
Deep active learning (AL) selects batches of instances for annotation to avoid retraining deep neural networks (DNNs) after each new label. Employing a naive top- selection can…
cs.LG2025
Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios
Ben Gerhards, Nikita Popkov, Annekatrin König +5
Forecasting attracts a lot of research attention in the electricity value chain. However, most studies concentrate on short-term forecasting of generation or consumption with a foc…