Building Robust Classifiers with Generative Adversarial Networks for Detecting Cavitation in Hydraulic Turbines. - Andreas Look; Oliver Kirschner; Stefan Riedelbauch (ICPRAM, 2018)

Dealing with Limited Access to Data: Comparison of Deep Learning Approaches. - Andreas Look; Stefan Riedelbauch (IJCNN, 2019)

Differential Bayesian Neural Nets. - Andreas Look; Melih Kandemir (CoRR, 2019)

Deterministic Inference of Neural Stochastic Differential Equations. - Andreas Look; Chen Qiu 001; Maja Rudolph; Jan Peters 001; Melih Kandemir (CoRR, 2020)

Differentiable Implicit Layers. - Andreas Look; Simona Doneva; Melih Kandemir; Rainer Gemulla; Jan Peters 001 (CoRR, 2020)

Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. - Manuel Haußmann; Sebastian Gerwinn; Andreas Look; Barbara Rakitsch; Melih Kandemir (AISTATS, 2021)

Can you text what is happening? Integrating pre-trained language encoders into trajectory prediction models for autonomous driving. - Ali Keysan; Andreas Look; Eitan Kosman; Gonca Gürsun; Jörg Wagner; Yu Yao; Barbara Rakitsch (CoRR, 2023)

Sampling-Free Probabilistic Deep State-Space Models. - Andreas Look; Melih Kandemir; Barbara Rakitsch; Jan Peters 001 (CoRR, 2023)

Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems. - Andreas Look; Barbara Rakitsch; Melih Kandemir; Jan Peters 001 (Trans. Mach. Learn. Res., 2023)

Deterministic Approximations for Deep State-Space Models. - Andreas Look (2023)

A Deterministic Approximation to Neural SDEs. - Andreas Look; Melih Kandemir; Barbara Rakitsch; Jan Peters 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Motion Forecasting via Model-Based Risk Minimization. - Aron Distelzweig; Eitan Kosman; Andreas Look; Faris Janjos; Denesh K. Manivannan; Abhinav Valada (CoRR, 2024)

Entropy-Based Uncertainty Modeling for Trajectory Prediction in Autonomous Driving. - Aron Distelzweig; Andreas Look; Eitan Kosman; Faris Janjos; Jörg Wagner; Abhinav Valada (CoRR, 2024)