54 citations · 225 across the 24 of their papers we have counts for
25 papers
Post-Training Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker Decomposition
Tobias Weber, Jakob Dexl, David Rügamer +1
We address the computational barrier of deploying advanced deep learning segmentation models in clinical settings by studying the efficacy of network compression through tensor dec…
Bayesian Semi-structured Subspace Inference
Daniel Dold, David Rügamer, Beate Sick +1
Semi-structured regression models enable the joint modeling of interpretable structured and complex unstructured feature effects. The structured model part is inspired by statistic…
Functional Data Analysis: An Introduction and Recent Developments
Jan Gertheiss, David Rügamer, Bernard X. W. Liew +1
Functional data analysis (FDA) is a statistical framework that allows for the analysis of curves, images, or functions on higher dimensional domains. The goals of FDA, such as desc…
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry
Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou +3
Bayesian inference in deep neural networks is challenging due to the high-dimensional, strongly multi-modal parameter posterior density landscape. Markov chain Monte Carlo approach…
Fusing Structure from Motion and Simulation-Augmented Pose Regression from Optical Flow for Challenging Indoor Environments
Felix Ott, Lucas Heublein, David Rügamer +2
The localization of objects is a crucial task in various applications such as robotics, virtual and augmented reality, and the transportation of goods in warehouses. Recent advance…
Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition
Felix Ott, David Rügamer, Lucas Heublein +2
The performance of a machine learning model degrades when it is applied to data from a similar but different domain than the data it has initially been trained on. The goal of doma…
Mixture of Experts Distributional Regression: Implementation Using Robust Estimation with Adaptive First-order Methods
David Rügamer, Florian Pfisterer, Bernd Bischl +1
In this work, we propose an efficient implementation of mixtures of experts distributional regression models which exploits robust estimation by using stochastic first-order optimi…
ARMA Cell: A Modular and Effective Approach for Neural Autoregressive Modeling
Philipp Schiele, Christoph Berninger, David Rügamer
The autoregressive moving average (ARMA) model is a classical, and arguably one of the most studied approaches to model time series data. It has compelling theoretical properties a…
Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression
Felix Ott, Nisha Lakshmana Raichur, David Rügamer +4
Visual-inertial localization is a key problem in computer vision and robotics applications such as virtual reality, self-driving cars, and aerial vehicles. The goal is to estimate…
Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift
Andreas Klaß, Sven M. Lorenz, Martin W. Lauer-Schmaltz +4
For many applications, analyzing the uncertainty of a machine learning model is indispensable. While research of uncertainty quantification (UQ) techniques is very advanced for com…
Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift
Felix Ott, David Rügamer, Lucas Heublein +2
The performance of a machine learning model degrades when it is applied to data from a similar but different domain than the data it has initially been trained on. To mitigate this…
Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition
Felix Ott, David Rügamer, Lucas Heublein +2
Cross-modal representation learning learns a shared embedding between two or more modalities to improve performance in a given task compared to using only one of the modalities. Cr…
Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens
Felix Ott, David Rügamer, Lucas Heublein +4
Purpose. Handwriting is one of the most frequently occurring patterns in everyday life and with it come challenging applications such as handwriting recognition (HWR), writer ident…
Probabilistic Time Series Forecasts with Autoregressive Transformation Models
David Rügamer, Philipp F. M. Baumann, Thomas Kneib +1
Probabilistic forecasting of time series is an important matter in many applications and research fields. In order to draw conclusions from a probabilistic forecast, we must ensure…
Joint Debiased Representation Learning and Imbalanced Data Clustering
Mina Rezaei, Emilio Dorigatti, David Ruegamer +1
One of the most promising approaches for unsupervised learning is combining deep representation learning and deep clustering. Some recent works propose to simultaneously learn repr…
deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression
David Rügamer, Chris Kolb, Cornelius Fritz +11
In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of…
Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany
Cornelius Fritz, Emilio Dorigatti, David Rügamer
During 2020, the infection rate of COVID-19 has been investigated by many scholars from different research fields. In this context, reliable and interpretable forecasts of disease…
Semi-Structured Deep Piecewise Exponential Models
Philipp Kopper, Sebastian Pölsterl, Christian Wachinger +3
We propose a versatile framework for survival analysis that combines advanced concepts from statistics with deep learning. The presented framework is based on piecewise exponential…
Neural Mixture Distributional Regression
David Rügamer, Florian Pfisterer, Bernd Bischl
We present neural mixture distributional regression (NMDR), a holistic framework to estimate complex finite mixtures of distributional regressions defined by flexible additive pred…
Deep Conditional Transformation Models
Philipp F. M. Baumann, Torsten Hothorn, David Rügamer
Learning the cumulative distribution function (CDF) of an outcome variable conditional on a set of features remains challenging, especially in high-dimensional settings. Conditiona…
A General Machine Learning Framework for Survival Analysis
Andreas Bender, David Rügamer, Fabian Scheipl +1
The modeling of time-to-event data, also known as survival analysis, requires specialized methods that can deal with censoring and truncation, time-varying features and effects, an…
Semi-Structured Distributional Regression -- Extending Structured Additive Models by Arbitrary Deep Neural Networks and Data Modalities
David Rügamer, Chris Kolb, Nadja Klein
Combining additive models and neural networks allows to broaden the scope of statistical regression and extend deep learning-based approaches by interpretable structured additive p…
Conditional Model Selection in Mixed-Effects Models with cAIC4
Benjamin Säfken, David Rügamer, Thomas Kneib +1
Model selection in mixed models based on the conditional distribution is appropriate for many practical applications and has been a focus of recent statistical research. In this pa…
Boosting Functional Regression Models with FDboost
Sarah Brockhaus, David Rügamer, Sonja Greven
The R add-on package FDboost is a flexible toolbox for the estimation of functional regression models by model-based boosting. It provides the possibility to fit regression models…