Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Rakitsch, Barbara"'
Active learning (AL) is a sequential learning scheme aiming to select the most informative data. AL reduces data consumption and avoids the cost of labeling large amounts of data. However, AL trains the model and solves an acquisition optimization fo
Externí odkaz:
http://arxiv.org/abs/2407.17992
Sequential learning methods such as active learning and Bayesian optimization select the most informative data to learn about a task. In many medical or engineering applications, the data selection is constrained by a priori unknown safety conditions
Externí odkaz:
http://arxiv.org/abs/2402.14402
Design patterns provide a systematic way to convey solutions to recurring modeling challenges. This paper introduces design patterns for hybrid modeling, an approach that combines modeling based on first principles with data-driven modeling technique
Externí odkaz:
http://arxiv.org/abs/2401.00033
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 Markovian dynamics. A Probabilistic Deep SSM (ProDSSM) generalizes this fra
Externí odkaz:
http://arxiv.org/abs/2309.08256
Autor:
Keysan, Ali, Look, Andreas, Kosman, Eitan, Gürsun, Gonca, Wagner, Jörg, Yao, Yu, Rakitsch, Barbara
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 scene and extract its features are still open research questions. In this
Externí odkaz:
http://arxiv.org/abs/2309.05282
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 much progress made for deterministic interacting systems, modeling is mu
Externí odkaz:
http://arxiv.org/abs/2305.01773
Autor:
Ensinger, Katharina, Ziesche, Sebastian, Rakitsch, Barbara, Tiemann, Michael, Trimpe, Sebastian
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 these models typically provide exact short-term predictions, accumulati
Externí odkaz:
http://arxiv.org/abs/2302.13754
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 accurately from their interactions. By employing latent Gaussian process ordi
Externí odkaz:
http://arxiv.org/abs/2205.11894
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 modeling these complex systems since they can exploit the inherent correlat
Externí odkaz:
http://arxiv.org/abs/2203.14849
Autor:
Longi, Krista, Lindinger, Jakob, Duennbier, Olaf, Kandemir, Melih, Klami, Arto, Rakitsch, Barbara
Gaussian Process state-space models capture complex temporal dependencies in a principled manner by placing a Gaussian Process prior on the transition function. These models have a natural interpretation as discretized stochastic differential equatio
Externí odkaz:
http://arxiv.org/abs/2112.03230