Search Results for author: Martin Schiegg

Found 5 papers, 1 papers with code

Validation of Composite Systems by Discrepancy Propagation

no code implementations21 Oct 2022 David Reeb, Kanil Patel, Karim Barsim, Martin Schiegg, Sebastian Gerwinn

Assessing the validity of a real-world system with respect to given quality criteria is a common yet costly task in industrial applications due to the vast number of required real-world tests.

Experimental Design

Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems

no code implementations ICML 2020 Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann, Philipp Hennig

To address this shortcoming, we employ Gaussian ODE filtering (a probabilistic numerical method for ODEs) to construct a local Gaussian approximation to the likelihood.

Relational Generalized Few-Shot Learning

no code implementations22 Jul 2019 Xiahan Shi, Leonard Salewski, Martin Schiegg, Zeynep Akata, Max Welling

Instead, we consider the extended setup of generalized few-shot learning (GFSL), where the model is required to perform classification on the joint label space consisting of both previously seen and novel classes.

Few-Shot Learning Generalized Few-Shot Learning

Probabilistic Recurrent State-Space Models

2 code implementations ICML 2018 Andreas Doerr, Christian Daniel, Martin Schiegg, Duy Nguyen-Tuong, Stefan Schaal, Marc Toussaint, Sebastian Trimpe

State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification.

Gaussian Processes Time Series +1

Tracking Indistinguishable Translucent Objects over Time using Weakly Supervised Structured Learning

no code implementations CVPR 2014 Luca Fiaschi, Ferran Diego, Konstantin Gregor, Martin Schiegg, Ullrich Koethe, Marta Zlatic, Fred A. Hamprecht

We use weakly supervised structured learning to track and disambiguate the identity of multiple indistinguishable, translucent and deformable objects that can overlap for many frames.

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