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Books and Theses
- 2023
[b1]Andreas Look:
Deterministic Approximations for Deep State-Space Models. Technical University of Darmstadt, Germany, 2023
Journal Articles
- 2023
- [j2]Andreas Look
, Melih Kandemir, Barbara Rakitsch, Jan Peters:
A Deterministic Approximation to Neural SDEs. IEEE Trans. Pattern Anal. Mach. Intell. 45(4): 4023-4037 (2023) - [j1]Andreas Look, Barbara Rakitsch, Melih Kandemir, Jan Peters:
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems. Trans. Mach. Learn. Res. 2023 (2023)
Conference and Workshop Papers
- 2021
- [c3]Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir:
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. AISTATS 2021: 478-486 - 2019
- [c2]Andreas Look, Stefan Riedelbauch:
Dealing with Limited Access to Data: Comparison of Deep Learning Approaches. IJCNN 2019: 1-8 - 2018
- [c1]Andreas Look, Oliver Kirschner, Stefan Riedelbauch:
Building Robust Classifiers with Generative Adversarial Networks for Detecting Cavitation in Hydraulic Turbines. ICPRAM 2018: 456-462
Informal and Other Publications
- 2024
- [i9]Aron Distelzweig, Eitan Kosman, Andreas Look, Faris Janjos, Denesh K. Manivannan, Abhinav Valada:
Motion Forecasting via Model-Based Risk Minimization. CoRR abs/2409.10585 (2024) - [i8]Aron Distelzweig, Andreas Look, Eitan Kosman, Faris Janjos, Jörg Wagner, Abhinav Valada:
Entropy-Based Uncertainty Modeling for Trajectory Prediction in Autonomous Driving. CoRR abs/2410.01628 (2024) - 2023
- [i7]Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters:
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems. CoRR abs/2305.01773 (2023) - [i6]Ali Keysan, Andreas Look, Eitan Kosman, Gonca Gürsun, Jörg Wagner, Yu Yao, Barbara Rakitsch:
Can you text what is happening? Integrating pre-trained language encoders into trajectory prediction models for autonomous driving. CoRR abs/2309.05282 (2023) - [i5]Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters:
Sampling-Free Probabilistic Deep State-Space Models. CoRR abs/2309.08256 (2023) - 2020
- [i4]Andreas Look, Chen Qiu, Maja Rudolph, Jan Peters, Melih Kandemir:
Deterministic Inference of Neural Stochastic Differential Equations. CoRR abs/2006.08973 (2020) - [i3]Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir:
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. CoRR abs/2006.09914 (2020) - [i2]Andreas Look, Simona Doneva, Melih Kandemir, Rainer Gemulla, Jan Peters:
Differentiable Implicit Layers. CoRR abs/2010.07078 (2020) - 2019
- [i1]Andreas Look, Melih Kandemir:
Differential Bayesian Neural Nets. CoRR abs/1912.00796 (2019)
Coauthor Index
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