no code implementations • 15 Sep 2023 • Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters
Many real-world dynamical systems can be described as State-Space Models (SSMs).
no code implementations • 11 Sep 2023 • Ali Keysan, Andreas Look, Eitan Kosman, Gonca Gürsun, Jörg Wagner, Yu Yao, Barbara Rakitsch
In autonomous driving tasks, scene understanding is the first step towards predicting the future behavior of the surrounding traffic participants.
1 code implementation • 2 May 2023 • Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters
Furthermore, we propose structured approximations to the covariance matrices of the Gaussian components in order to scale up to systems with many agents.
no code implementations • 14 Oct 2020 • Andreas Look, Simona Doneva, Melih Kandemir, Rainer Gemulla, Jan Peters
In this paper, we introduce an efficient backpropagation scheme for non-constrained implicit functions.
no code implementations • 17 Jun 2020 • Manuel Haussmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir
Neural Stochastic Differential Equations model a dynamical environment with neural nets assigned to their drift and diffusion terms.
no code implementations • 16 Jun 2020 • Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters
Our deterministic approximation of the transition kernel is applicable to both training and prediction.
no code implementations • 2 Dec 2019 • Andreas Look, Melih Kandemir
Neural Ordinary Differential Equations (N-ODEs) are a powerful building block for learning systems, which extend residual networks to a continuous-time dynamical system.