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2020 – today
- 2023
[j4]Joachim Giesen, Paul Kahlmeyer, Sören Laue, Matthias Mitterreiter, Frank Nussbaum
, Christoph Staudt:
Mixed membership Gaussians. J. Multivar. Anal. 195: 105141 (2023)- [j3]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): 526-536 (2023) - [c8]Matthias Mitterreiter, Marcel Koch, Joachim Giesen, Sören Laue:
Why Capsule Neural Networks Do Not Scale: Challenging the Dynamic Parse-Tree Assumption. AAAI 2023: 9209-9216 - [i3]Matthias Mitterreiter, Marcel Koch, Joachim Giesen, Sören Laue:
Why Capsule Neural Networks Do Not Scale: Challenging the Dynamic Parse-Tree Assumption. CoRR abs/2301.01583 (2023) - 2021
- [c7]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: 2418-2424 - 2020
- [j2]Sören Laue, Matthias Mitterreiter, Joachim Giesen:
A Simple and Efficient Tensor Calculus for Machine Learning. Fundam. Informaticae 177(2): 157-179 (2020) - [j1]Joachim Giesen, Sören Laue, Matthias Mitterreiter:
Optimization frameworks for machine learning: Examples and case study. it Inf. Technol. 62(3-4): 169-180 (2020) - [c6]Sören Laue, Matthias Mitterreiter, Joachim Giesen:
A Simple and Efficient Tensor Calculus. AAAI 2020: 4527-4534 - [c5]Sören Laue, Matthias Mitterreiter, Joachim Giesen:
GENO - Optimization for Classical Machine Learning Made Fast and Easy. AAAI 2020: 13620-13621 - [c4]Mark Blacher, Joachim Giesen, Sören Laue, Julien Klaus, Matthias Mitterreiter:
Fast Entity Resolution With Mock Labels and Sorted Integer Sets. DI2KG@VLDB 2020 - [i2]Sören Laue, Matthias Mitterreiter, Joachim Giesen:
A Simple and Efficient Tensor Calculus for Machine Learning. CoRR abs/2010.03313 (2020)
2010 – 2019
- 2019
- [c3]Sören Laue, Matthias Mitterreiter, Joachim Giesen:
GENO - GENeric Optimization for Classical Machine Learning. NeurIPS 2019: 2187-2198 - [c2]Sören Laue, Matthias Mitterreiter, Joachim Giesen:
MatrixCalculus.org - Computing Derivatives of Matrix and Tensor Expressions. ECML/PKDD (3) 2019: 769-772 - [i1]Sören Laue, Matthias Mitterreiter, Joachim Giesen:
GENO - GENeric Optimization for Classical Machine Learning. CoRR abs/1905.13587 (2019) - 2018
- [c1]Sören Laue, Matthias Mitterreiter, Joachim Giesen:
Computing Higher Order Derivatives of Matrix and Tensor Expressions. NeurIPS 2018: 2755-2764
Coauthor Index
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