128 citations · 204 across the 9 of their papers we have counts for
9 papers
Plasma State Monitoring and Disruption Characterization using Multimodal VAEs
Yoeri Poels, Alessandro Pau, Christian Donner +6
When a plasma disrupts in a tokamak, significant heat and electromagnetic loads are deposited onto the surrounding device components. These forces scale with plasma current and mag…
A projected nonlinear state-space model for forecasting time series signals
Christian Donner, Anuj Mishra, Hideaki Shimazaki
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…
GP-ETAS: Semiparametric Bayesian inference for the spatio-temporal Epidemic Type Aftershock Sequence model
Christian Molkenthin, Christian Donner, Sebastian Reich +4
The spatio-temporal Epidemic Type Aftershock Sequence (ETAS) model is widely used to describe the self-exciting nature of earthquake occurrences. While traditional inference method…
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
Théo Galy-Fajou, Florian Wenzel, Christian Donner +1
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 lea…
Smoothed Dilated Convolutions for Improved Dense Prediction
Zhengyang Wang, Shuiwang Ji
Dilated convolutions, also known as atrous convolutions, have been widely explored in deep convolutional neural networks (DCNNs) for various dense prediction tasks. However, dilate…
Efficient Bayesian Inference for a Gaussian Process Density Model
Christian Donner, Manfred Opper
We reconsider a nonparametric density model based on Gaussian processes. By augmenting the model with latent Pólya--Gamma random variables and a latent marked Poisson process we ob…
Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation
Florian Wenzel, Theo Galy-Fajou, Christan Donner +2
We propose a scalable stochastic variational approach to GP classification building on Polya-Gamma data augmentation and inducing points. Unlike former approaches, we obtain closed…
Inverse Ising problem in continuous time: A latent variable approach
Christian Donner, Manfred Opper
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…
Approximate Inference for Time-varying Interactions and Macroscopic Dynamics of Neural Populations
Christian Donner, Klaus Obermayer, Hideaki Shimazaki
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 b…