activity
20162024
most citedConditional Model Selection in Mixed-Effects Models with cAIC4

54 citations · 225 across the 24 of their papers we have counts for

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Showing 2022Show all

7 papers · 1 filter

stat.CO2022★ 4 cited

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…

cs.LG2022

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…

cs.CV2022★ 4 cited

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…

cs.CV2022★ 5 cited

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…

cs.LG2022★ 36 cited

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…

cs.LG2022★ 15 cited

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…

cs.LG2022★ 28 cited

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…