34 citations · 57 across the 5 of their papers we have counts for
5 papers
ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing
Kiran Madhusudhanan, Gunnar Behrens, Maximilian Stubbemann +1
Used car pricing is a critical aspect of the automotive industry, influenced by many economic factors and market dynamics. With the recent surge in online marketplaces and increase…
Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties with Deep Learning Multi-Member and Stochastic Parameterizations
Gunnar Behrens, Tom Beucler, Fernando Iglesias-Suarez +5
Deep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here…
ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation
Sungduk Yu, Zeyuan Hu, Akshay Subramaniam +44
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderst…
Non-Linear Dimensionality Reduction with a Variational Encoder Decoder to Understand Convective Processes in Climate Models
Gunnar Behrens, Tom Beucler, Pierre Gentine +3
Deep learning can accurately represent sub-grid-scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack…
Positive-Unlabeled Domain Adaptation
Jonas Sonntag, Gunnar Behrens, Lars Schmidt-Thieme
Domain Adaptation methodologies have shown to effectively generalize from a labeled source domain to a label scarce target domain. Previous research has either focused on unlabeled…