1 code implementation • 6 Mar 2024 • Arik Reuter, Anton Thielmann, Christoph Weisser, Benjamin Säfken, Thomas Kneib
With the rise of transformers in Natural Language Processing, however, several successful models that rely on straightforward clustering approaches in transformer-based embedding spaces have emerged and consolidated the notion of topics as clusters of embedding vectors.
1 code implementation • 15 Sep 2023 • Jonathan Henrich, Jan van Delden, Dominik Seidel, Thomas Kneib, Alexander Ecker
We trained TreeLearn on forest point clouds of 6665 trees, labeled using the Lidar360 software.
1 code implementation • 27 Jan 2023 • Anton Thielmann, René-Marcel Kruse, Thomas Kneib, Benjamin Säfken
We propose Neural Additive Models for Location Scale and Shape (NAMLSS), a modelling framework that combines the predictive power of classical deep learning models with the inherent advantages of distributional regression while maintaining the interpretability of additive models.
1 code implementation • 8 Jul 2022 • Andreas Buchmüller, Gillian Kant, Christoph Weisser, Benjamin Säfken, Krisztina Kis-Katos, Thomas Kneib
We present Twitmo, a package that provides a broad range of methods to collect, pre-process, analyze and visualize geo-tagged Twitter data.
1 code implementation • 17 May 2022 • Manuel Carlan, Thomas Kneib
For count responses, the resulting transformation model is novel in the sense that it is a Bayesian fully parametric yet distribution-free approach that can additionally account for excess zeros with additive transformation function specifications.
2 code implementations • 2 Apr 2022 • Alexander März, Thomas Kneib
We present a unified probabilistic gradient boosting framework for regression tasks that models and predicts the entire conditional distribution of a univariate response variable as a function of covariates.
no code implementations • 15 Oct 2021 • David Rügamer, Philipp F. M. Baumann, Thomas Kneib, Torsten Hothorn
Probabilistic forecasting of time series is an important matter in many applications and research fields.
no code implementations • 29 Sep 2021 • Anton Frederik Thielmann, Christoph Weisser, Thomas Kneib, Benjamin Saefken
While these algorithms differ in their modeling approach, they have in common that hyperparameter optimization is difficult and is mainly achieved by maximizing the extracted topic coherence scores via a grid search.
no code implementations • 14 Jan 2021 • Paul Wiemann, Thomas Kneib
In this paper, we propose a new horseshoe-type prior hierarchy for adaptively shrinking spline-based functional effects towards a predefined vector space of parametric functions.
Methodology
1 code implementation • 20 Dec 2020 • Manuel Carlan, Thomas Kneib, Nadja Klein
A simulation study demonstrates the competitiveness of our approach against its likelihood-based counterpart but also Bayesian additive models of location, scale and shape and Bayesian quantile regression.
Methodology
1 code implementation • 9 Sep 2016 • Elisabeth Waldmann, David Taylor-Robinson, Nadja Klein, Thomas Kneib, Tania Pressler, Matthias Schmid, Andreas Mayr
Joint Models for longitudinal and time-to-event data have gained a lot of attention in the last few years as they are a helpful technique to approach common a data structure in clinical studies where longitudinal outcomes are recorded alongside event times.
1 code implementation • 26 May 2011 • Fabian Scheipl, Ludwig Fahrmeir, Thomas Kneib
Structured additive regression provides a general framework for complex Gaussian and non-Gaussian regression models, with predictors comprising arbitrary combinations of nonlinear functions and surfaces, spatial effects, varying coefficients, random effects and further regression terms.
Methodology Applications