Matthias Mitterreiter

Publication Activity (10 Years)

Years Active: 2018-2023
Publications (10 Years): 15

Top Topics

Mixed Membership

Machine Learning

Optimization Approaches

Top Venues

Fundam. Informaticae

Publications

Kai Lawonn, Monique Meuschke, Pepe Eulzer, Matthias Mitterreiter, Joachim Giesen, Tobias Günther
GRay: Ray Casting for Visualization and Interactive Data Exploration of Gaussian Mixture Models. IEEE Trans. Vis. Comput. Graph. 29 (1) (2023)
Matthias Mitterreiter, Marcel Koch, Joachim Giesen, Sören Laue
Why Capsule Neural Networks Do Not Scale: Challenging the Dynamic Parse-Tree Assumption. CoRR (2023)
Joachim Giesen, Paul Kahlmeyer, Sören Laue, Matthias Mitterreiter, Frank Nussbaum, Christoph Staudt
Mixed membership Gaussians. J. Multivar. Anal. 195 (2023)
Matthias Mitterreiter, Marcel Koch, Joachim Giesen, Sören Laue
Why Capsule Neural Networks Do Not Scale: Challenging the Dynamic Parse-Tree Assumption. AAAI (2023)
Joachim Giesen, Paul Kahlmeyer, Sören Laue, Matthias Mitterreiter, Frank Nussbaum, Christoph Staudt, Sina Zarrieß
Method of Moments for Topic Models with Mixed Discrete and Continuous Features. IJCAI (2021)
Joachim Giesen, Sören Laue, Matthias Mitterreiter
Optimization frameworks for machine learning: Examples and case study. it Inf. Technol. 62 (3-4) (2020)
Sören Laue, Matthias Mitterreiter, Joachim Giesen
A Simple and Efficient Tensor Calculus for Machine Learning. CoRR (2020)
Sören Laue, Matthias Mitterreiter, Joachim Giesen
A Simple and Efficient Tensor Calculus. AAAI (2020)
Mark Blacher, Joachim Giesen, Sören Laue, Julien Klaus, Matthias Mitterreiter
Fast Entity Resolution With Mock Labels and Sorted Integer Sets. DI2KG@VLDB (2020)
Sören Laue, Matthias Mitterreiter, Joachim Giesen
GENO - Optimization for Classical Machine Learning Made Fast and Easy. AAAI (2020)
Sören Laue, Matthias Mitterreiter, Joachim Giesen
A Simple and Efficient Tensor Calculus for Machine Learning. Fundam. Informaticae 177 (2) (2020)
Sören Laue, Matthias Mitterreiter, Joachim Giesen
MatrixCalculus.org - Computing Derivatives of Matrix and Tensor Expressions. ECML/PKDD (3) (2019)
Sören Laue, Matthias Mitterreiter, Joachim Giesen
GENO - GENeric Optimization for Classical Machine Learning. CoRR (2019)
Sören Laue, Matthias Mitterreiter, Joachim Giesen
GENO - GENeric Optimization for Classical Machine Learning. NeurIPS (2019)
Sören Laue, Matthias Mitterreiter, Joachim Giesen
Computing Higher Order Derivatives of Matrix and Tensor Expressions. NeurIPS (2018)