Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Donner, Christian"'
Learning and forecasting stochastic time series is essential in various scientific fields. However, despite the proposals of nonlinear filters and deep-learning methods, it remains challenging to capture nonlinear dynamics from a few noisy samples an
Externí odkaz:
http://arxiv.org/abs/2311.13247
In the development of locally resonant metamaterials, the physical resonator design is often omitted and replaced by an idealized mass-spring system. This paper presents a novel approach for designing multimodal resonant structures, which give rise t
Externí odkaz:
http://arxiv.org/abs/2309.04177
Autor:
Hornauer, Philipp, Prack, Gustavo, Anastasi, Nadia, Ronchi, Silvia, Kim, Taehoon, Donner, Christian, Fiscella, Michele, Borgwardt, Karsten, Taylor, Verdon, Jagasia, Ravi, Roqueiro, Damian, Hierlemann, Andreas, Schröter, Manuel
Publikováno v:
In Stem Cell Reports 13 February 2024 19(2):285-298
Autor:
Donner, Christian1 (AUTHOR) christian.research@mailbox.org, Bartram, Julian2 (AUTHOR), Hornauer, Philipp2 (AUTHOR), Kim, Taehoon2 (AUTHOR), Roqueiro, Damian2 (AUTHOR), Hierlemann, Andreas2 (AUTHOR), Obozinski, Guillaume1 (AUTHOR), Schröter, Manuel2 (AUTHOR) christian.research@mailbox.org
Publikováno v:
PLoS Computational Biology. 4/29/2024, Vol. 20 Issue 4, p1-25. 25p.
Autor:
Molkenthin, Christian, Donner, Christian, Reich, Sebastian, Zöller, Gert, Hainzl, Sebastian, Holschneider, Matthias, Opper, Manfred
The spatio-temporal Epidemic Type Aftershock Sequence (ETAS) model is widely used to describe the self-exciting nature of earthquake occurrences. While traditional inference methods provide only point estimates of the model parameters, we aim at a fu
Externí odkaz:
http://arxiv.org/abs/2005.12857
We propose a new scalable multi-class Gaussian process classification approach building on a novel modified softmax likelihood function. The new likelihood has two benefits: it leads to well-calibrated uncertainty estimates and allows for an efficien
Externí odkaz:
http://arxiv.org/abs/1905.09670
Autor:
Donner, Christian, Opper, Manfred
Publikováno v:
Journal of Machine Learning Research, year 2018, volume 19,number 67, pages 1-34
We present an approximate Bayesian inference approach for estimating the intensity of an inhomogeneous Poisson process, where the intensity function is modelled using a Gaussian process (GP) prior via a sigmoid link function. Augmenting the model usi
Externí odkaz:
http://arxiv.org/abs/1808.00831
Autor:
Donner, Christian, Opper, Manfred
We reconsider a nonparametric density model based on Gaussian processes. By augmenting the model with latent P\'olya--Gamma random variables and a latent marked Poisson process we obtain a new likelihood which is conjugate to the model's Gaussian pro
Externí odkaz:
http://arxiv.org/abs/1805.11494
Autor:
Donner, Christian, Opper, Manfred
Publikováno v:
Phys. Rev. E 96, 062104 (2017)
We consider the inverse Ising problem, i.e. the inference of network couplings from observed spin trajectories for a model with continuous time Glauber dynamics. By introducing two sets of auxiliary latent random variables we render the likelihood in
Externí odkaz:
http://arxiv.org/abs/1709.04495
The models in statistical physics such as an Ising model offer a convenient way to characterize stationary activity of neural populations. Such stationary activity of neurons may be expected for recordings from in vitro slices or anesthetized animals
Externí odkaz:
http://arxiv.org/abs/1607.08840