Consistent and accurate estimation of stellar parameters from HARPS-N Spectroscopy using Deep Learning. - Frederik Boe Hüttel; Line Katrine Harder Clemmensen (NLDL, 2021)

Deep Spatio-Temporal Forecasting of Electrical Vehicle Charging Demand. - Frederik Boe Hüttel; Inon Peled; Filipe Rodrigues 001; Francisco C. Pereira (CoRR, 2021)

Bayesian Active Learning with Fully Bayesian Gaussian Processes. - Christoffer Riis; Francisco Antunes; Frederik Boe Hüttel; Carlos Lima Azevedo; Francisco Pereira 002 (NeurIPS, 2022)

Modeling Censored Mobility Demand Through Censored Quantile Regression Neural Networks. - Frederik Boe Hüttel; Inon Peled; Filipe Rodrigues 001; Francisco C. Pereira (IEEE Trans. Intell. Transp. Syst., 2022)

Mind the Gap - Modelling Difference Between Censored and Uncensored Electric Vehicle Charging Demand. - Frederik Boe Hüttel; Filipe Rodrigues 001; Francisco Câmara Pereira (CoRR, 2023)

Deep Evidential Learning for Bayesian Quantile Regression. - Frederik Boe Hüttel; Filipe Rodrigues 001; Francisco Câmara Pereira (CoRR, 2023)

Bayesian Active Learning for Censored Regression. - Frederik Boe Hüttel; Christoffer Riis; Filipe Rodrigues 001; Francisco Câmara Pereira (CoRR, 2024)