21 citations · 49 across the 13 of their papers we have counts for
13 papers
Sampling-Free Probabilistic Deep State-Space Models
Andreas Look, Melih Kandemir, Barbara Rakitsch +1
Many real-world dynamical systems can be described as State-Space Models (SSMs). In this formulation, each observation is emitted by a latent state, which follows first-order Marko…
Can you text what is happening? Integrating pre-trained language encoders into trajectory prediction models for autonomous driving
Ali Keysan, Andreas Look, Eitan Kosman +4
In autonomous driving tasks, scene understanding is the first step towards predicting the future behavior of the surrounding traffic participants. Yet, how to represent a given sce…
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems
Andreas Look, Melih Kandemir, Barbara Rakitsch +1
Graph neural networks are often used to model interacting dynamical systems since they gracefully scale to systems with a varying and high number of agents. While there has been mu…
Combining Slow and Fast: Complementary Filtering for Dynamics Learning
Katharina Ensinger, Sebastian Ziesche, Barbara Rakitsch +2
Modeling an unknown dynamical system is crucial in order to predict the future behavior of the system. A standard approach is training recurrent models on measurement data. While t…
Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs
Çağatay Yıldız, Melih Kandemir, Barbara Rakitsch
We study time uncertainty-aware modeling of continuous-time dynamics of interacting objects. We introduce a new model that decomposes independent dynamics of single objects accurat…
Safe Active Learning for Multi-Output Gaussian Processes
Cen-You Li, Barbara Rakitsch, Christoph Zimmer
Multi-output regression problems are commonly encountered in science and engineering. In particular, multi-output Gaussian processes have been emerged as a promising tool for model…