Zobrazeno 1 - 10
of 371
pro vyhledávání: '"Zimmer, Christoph"'
We investigate the use of derivative information for Batch Active Learning in Gaussian Process regression models. The proposed approach employs the predictive covariance matrix for selection of data batches to exploit full correlation of samples. We
Externí odkaz:
http://arxiv.org/abs/2408.01861
We present the construction of a sparse-compressed operator that approximates the solution operator of elliptic PDEs with rough coefficients. To derive the compressed operator, we construct a hierarchical basis of an approximate solution space, with
Externí odkaz:
http://arxiv.org/abs/2407.18671
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
Experimental exploration of high-cost systems with safety constraints, common in engineering applications, is a challenging endeavor. Data-driven models offer a promising solution, but acquiring the requisite data remains expensive and is potentially
Externí odkaz:
http://arxiv.org/abs/2405.10581
Active learning of physical systems must commonly respect practical safety constraints, which restricts the exploration of the design space. Gaussian Processes (GPs) and their calibrated uncertainty estimations are widely used for this purpose. In ma
Externí odkaz:
http://arxiv.org/abs/2402.18260
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
Learning time-series models is useful for many applications, such as simulation and forecasting. In this study, we consider the problem of actively learning time-series models while taking given safety constraints into account. For time-series modeli
Externí odkaz:
http://arxiv.org/abs/2402.06276
This paper presents a novel spatial discretisation method for the reliable and efficient simulation of Bose-Einstein condensates modelled by the Gross-Pitaevskii equation and the corresponding nonlinear eigenvector problem. The method combines the hi
Externí odkaz:
http://arxiv.org/abs/2309.11985
Learning the kernel parameters for Gaussian processes is often the computational bottleneck in applications such as online learning, Bayesian optimization, or active learning. Amortizing parameter inference over different datasets is a promising appr
Externí odkaz:
http://arxiv.org/abs/2306.09819
Learning precise surrogate models of complex computer simulations and physical machines often require long-lasting or expensive experiments. Furthermore, the modeled physical dependencies exhibit nonlinear and nonstationary behavior. Machine learning
Externí odkaz:
http://arxiv.org/abs/2303.10022