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09/15/2023
Sampling-Free Probabilistic Deep State-Space Models
Many real-world dynamical systems can be described as State-Space Models...
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09/11/2023
Can you text what is happening? Integrating pre-trained language encoders into trajectory prediction models for autonomous driving
In autonomous driving tasks, scene understanding is the first step towar...
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05/02/2023
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems
Graph neural networks are often used to model interacting dynamical syst...
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02/27/2023
Combining Slow and Fast: Complementary Filtering for Dynamics Learning
Modeling an unknown dynamical system is crucial in order to predict the ...
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05/24/2022
Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs
We study for the first time uncertainty-aware modeling of continuous-tim...
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03/28/2022
Safe Active Learning for Multi-Output Gaussian Processes
Multi-output regression problems are commonly encountered in science and...
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12/06/2021
Traversing Time with Multi-Resolution Gaussian Process State-Space Models
Gaussian Process state-space models capture complex temporal dependencie...
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06/17/2020
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes
We propose a novel scheme for fitting heavily parameterized non-linear s...
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05/22/2020
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties
Deep Gaussian Processes learn probabilistic data representations for sup...
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10/29/2018