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2020 – today
- 2024
[j18]Moritz Wolter, Felix Blanke, Jochen Garcke, Charles Tapley Hoyt:
ptwt - The PyTorch Wavelet Toolbox. J. Mach. Learn. Res. 25: 80:1-80:7 (2024)- [c30]Sara Hahner, Souhaib Attaiki, Jochen Garcke, Maks Ovsjanikov:
Unsupervised Representation Learning for Diverse Deformable Shape Collections. 3DV 2024: 1594-1604 - [c29]Arno Feiden
, Biagio Paparella, Jochen Garcke:
Generalizing Diversity with the Signature Transform. GECCO Companion 2024: 275-278 - [c28]Yanying Zhou, Jochen Garcke:
Learning Crowd Behaviors in Navigation with Attention-based Spatial-Temporal Graphs. ICRA 2024: 5485-5491 - [i20]Yanying Zhou, Jochen Garcke:
Learning Crowd Behaviors in Navigation with Attention-based Spatial-Temporal Graphs. CoRR abs/2401.06226 (2024) - 2023
- [j17]Sebastian Mayer, Léo Françoso Dal Piccol Sotto, Jochen Garcke:
The Elements of Flexibility for Task-Performing Systems. IEEE Access 11: 8029-8056 (2023) - [j16]Anahita Pakiman, Jochen Garcke, Axel Schumacher:
Knowledge discovery assistants for crash simulations with graph algorithms and energy absorption features. Appl. Intell. 53(16): 19217-19236 (2023) - [j15]Jochen Garcke, Ribana Roscher:
Explainable Machine Learning. Mach. Learn. Knowl. Extr. 5(1): 169-170 (2023) - [j14]Laura von Rüden, Sebastian Mayer, Katharina Beckh, Bogdan Georgiev, Sven Giesselbach, Raoul Heese, Birgit Kirsch, Julius Pfrommer, Annika Pick, Rajkumar Ramamurthy, Michal Walczak, Jochen Garcke, Christian Bauckhage, Jannis Schuecker:
Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems. IEEE Trans. Knowl. Data Eng. 35(1): 614-633 (2023) - [c27]Arno Feiden, Jochen Garcke:
Overcoming Deceptive Rewards with Quality-Diversity. GECCO Companion 2023: 279-282 - [c26]Anahita Pakiman, Jochen Garcke, Axel Schumacher:
Graph Extraction for Assisting Crash Simulation Data Analysis. ICCS 2023: 171-185 - [c25]Laura von Rüden, Jochen Garcke, Christian Bauckhage:
How Does Knowledge Injection Help in Informed Machine Learning? IJCNN 2023: 1-8 - [i19]Léo Françoso Dal Piccol Sotto, Sebastian Mayer, Hemanth Janarthanam, Alexander Butz, Jochen Garcke:
Evolutionary Solution Adaption for Multi-Objective Metal Cutting Process Optimization. CoRR abs/2305.19775 (2023) - [i18]Anahita Pakiman, Jochen Garcke, Axel Schumacher:
Graph Extraction for Assisting Crash Simulation Data Analysis. CoRR abs/2306.09538 (2023) - [i17]Niklas Breustedt, Paolo Climaco, Jochen Garcke, Jan Hamaekers, Gitta Kutyniok, Dirk A. Lorenz, Rick Oerder, Chirag Varun Shukla:
On the Interplay of Subset Selection and Informed Graph Neural Networks. CoRR abs/2306.10066 (2023) - [i16]Paolo Climaco, Jochen Garcke:
Investigating minimizing the training set fill distance in machine learning regression. CoRR abs/2307.10988 (2023) - [i15]Sara Hahner, Souhaib Attaiki, Jochen Garcke, Maks Ovsjanikov:
Unsupervised Representation Learning for Diverse Deformable Shape Collections. CoRR abs/2310.18141 (2023) - 2022
- [j13]Moritz Wolter, Felix Blanke, Raoul Heese, Jochen Garcke:
Wavelet-packets for deepfake image analysis and detection. Mach. Learn. 111(11): 4295-4327 (2022) - [c24]Léo Françoso Dal Piccol Sotto, Sebastian Mayer, Jochen Garcke:
The pole balancing problem from the viewpoint of system flexibility. GECCO Companion 2022: 427-430 - [c23]Anahita Pakiman, Jochen Garcke:
Graph Modeling in Computer Assisted Automotive Development. ICKG 2022: 203-210 - [c22]Lokesh Veeramacheneni, Moritz Wolter, Reinhard Klein, Jochen Garcke:
Canonical convolutional neural networks. IJCNN 2022: 1-8 - [c21]Sara Hahner, Felix Kerkhoff, Jochen Garcke:
Transfer Learning Using Spectral Convolutional Autoencoders on Semi-Regular Surface Meshes. LoG 2022: 18 - [c20]Sara Hahner, Jochen Garcke:
Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes. WACV 2022: 2344-2353 - [i14]Sebastian Mayer, Léo Françoso Dal Piccol Sotto, Jochen Garcke:
The elements of flexibility for task-performing systems. CoRR abs/2206.00582 (2022) - [i13]Lokesh Veeramacheneni, Moritz Wolter, Reinhard Klein, Jochen Garcke:
Canonical convolutional neural networks. CoRR abs/2206.01509 (2022) - [i12]Anahita Pakiman, Jochen Garcke:
Graph Modeling in Computer Assisted Automotive Development. CoRR abs/2209.14910 (2022) - [i11]Sara Hahner, Felix Kerkhoff, Jochen Garcke:
Transfer Learning using Spectral Convolutional Autoencoders on Semi-Regular Surface Meshes. CoRR abs/2212.05810 (2022) - 2021
- [c19]Moritz Wolter, Jochen Garcke:
Adaptive wavelet pooling for convolutional neural networks. AISTATS 2021: 1936-1944 - [c18]Arno Schmetz, Christopher Vahl, Zhen Zhen, Daniel Reibert, Sebastian Mayer, Daniel Zontar, Jochen Garcke, Christian Brecher:
Decision Support by Interpretable Machine Learning in Acoustic Emission Based Cutting Tool Wear Prediction. IEEM 2021: 629-633 - [i10]Yanying Zhou, Shijie Li, Jochen Garcke:
R-SARL: Crowd-aware Navigation Based Deep Reinforcement Learning for Nonholonomic Robot in Complex Environments. CoRR abs/2105.13409 (2021) - [i9]Moritz Wolter, Felix Blanke, Charles Tapley Hoyt, Jochen Garcke:
Wavelet-Packet Powered Deepfake Image Detection. CoRR abs/2106.09369 (2021) - [i8]Sara Hahner, Jochen Garcke:
Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes. CoRR abs/2110.09401 (2021) - 2020
- [j12]Ribana Roscher, Bastian Bohn, Marco F. Duarte, Jochen Garcke:
Explainable Machine Learning for Scientific Insights and Discoveries. IEEE Access 8: 42200-42216 (2020) - [c17]Skylar Sible, Rodrigo Iza-Teran, Jochen Garcke, Nikola Aulig, Patricia Wollstadt:
A Compact Spectral Descriptor for Shape Deformations. ECAI 2020: 1930-1937 - [c16]Sara Hahner, Rodrigo Iza-Teran, Jochen Garcke:
Analysis and Prediction of Deforming 3D Shapes Using Oriented Bounding Boxes and LSTM Autoencoders. ICANN (1) 2020: 284-296 - [c15]Laura von Rüden, Sebastian Mayer, Rafet Sifa, Christian Bauckhage, Jochen Garcke:
Combining Machine Learning and Simulation to a Hybrid Modelling Approach: Current and Future Directions. IDA 2020: 548-560 - [i7]Skylar Sible, Rodrigo Iza-Teran, Jochen Garcke, Nikola Aulig, Patricia Wollstadt:
A Compact Spectral Descriptor for Shape Deformations. CoRR abs/2003.08758 (2020) - [i6]Sara Hahner, Rodrigo Iza-Teran, Jochen Garcke:
Analysis and Prediction of Deforming 3D Shapes using Oriented Bounding Boxes and LSTM Autoencoders. CoRR abs/2009.03782 (2020)
2010 – 2019
- 2019
- [j11]Rodrigo Iza-Teran, Jochen Garcke:
A Geometrical Method for Low-Dimensional Representations of Simulations. SIAM/ASA J. Uncertain. Quantification 7(2): 472-496 (2019) - [i5]Rodrigo Iza-Teran, Jochen Garcke:
A Geometrical Method for Low-Dimensional Representations of Simulations. CoRR abs/1903.07744 (2019) - [i4]Laura von Rüden, Sebastian Mayer, Jochen Garcke, Christian Bauckhage, Jannis Schücker:
Informed Machine Learning - Towards a Taxonomy of Explicit Integration of Knowledge into Machine Learning. CoRR abs/1903.12394 (2019) - [i3]Ribana Roscher, Bastian Bohn, Marco F. Duarte, Jochen Garcke:
Explainable Machine Learning for Scientific Insights and Discoveries. CoRR abs/1905.08883 (2019) - 2018
- [c14]Friedrich Solowjow, Dominik Baumann, Jochen Garcke, Sebastian Trimpe:
Event-Triggered Learning for Resource-Efficient Networked Control. ACC 2018: 6506-6512 - [i2]Friedrich Solowjow, Dominik Baumann, Jochen Garcke, Sebastian Trimpe:
Event-triggered Learning for Resource-efficient Networked Control. CoRR abs/1803.01802 (2018) - 2017
- [j10]Jochen Garcke, Axel Kröner:
Suboptimal Feedback Control of PDEs by Solving HJB Equations on Adaptive Sparse Grids. J. Sci. Comput. 70(1): 1-28 (2017) - [p3]Jochen Garcke, Mandar Pathare, Nikhil Prabakaran:
ModelCompare. Scientific Computing and Algorithms in Industrial Simulations 2017: 199-205 - [p2]Jochen Garcke, Rodrigo Iza-Teran, Marvin Marks, Mandar Pathare, Dirk Schollbach, Martin Stettner:
Dimensionality Reduction for the Analysis of Time Series Data from Wind Turbines. Scientific Computing and Algorithms in Industrial Simulations 2017: 317-339 - [p1]Dustin Feld, Jochen Garcke, Jia Liu, Eric Schricker, Thomas Soddemann, Yong Xue:
Energy-Efficiency and Performance Comparison of Aerosol Optical Depth Retrieval on Distributed Embedded SoC Architectures. Scientific Computing and Algorithms in Industrial Simulations 2017: 341-358 - 2016
- [j9]Jia Liu, Dustin Feld, Yong Xue, Jochen Garcke, Thomas Soddemann, Peiyuan Pan:
An efficient geosciences workflow on multi-core processors and GPUs: a case study for aerosol optical depth retrieval from MODIS satellite data. Int. J. Digit. Earth 9(8): 748-765 (2016) - [j8]Alvaro Aguilera, Richard Grunzke, Dirk Habich, Johannes Luong, Dirk Schollbach, Ulf Markwardt, Jochen Garcke:
Advancing a Gateway Infrastructure for Wind Turbine Data Analysis. J. Grid Comput. 14(4): 499-514 (2016) - [j7]Bastian Bohn, Jochen Garcke, Michael Griebel:
A sparse grid based method for generative dimensionality reduction of high-dimensional data. J. Comput. Phys. 309: 1-17 (2016) - 2015
- [j6]Jia Liu, Dustin Feld, Yong Xue, Jochen Garcke, Thomas Soddemann:
Multicore Processors and Graphics Processing Unit Accelerators for Parallel Retrieval of Aerosol Optical Depth From Satellite Data: Implementation, Performance, and Energy Efficiency. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 8(5): 2306-2317 (2015) - [i1]Jochen Garcke:
VAVID - Handling Big Data. ERCIM News 2015(101) (2015) - 2014
- [c13]Rodrigo Iza-Teran, Jochen Garcke:
Data Analytics for Simulation Repositories in Industry. GI-Jahrestagung 2014: 161-167 - [c12]Jochen Garcke, Thomas Vanck:
Importance Weighted Inductive Transfer Learning for Regression. ECML/PKDD (1) 2014: 466-481 - 2013
- [j5]Olivier Bokanowski, Jochen Garcke, Michael Griebel, Irene Klompmaker:
An Adaptive Sparse Grid Semi-Lagrangian Scheme for First Order Hamilton-Jacobi Bellman Equations. J. Sci. Comput. 55(3): 575-605 (2013) - [c11]Thomas Vanck, Jochen Garcke:
Using Hyperbolic Cross Approximation to measure and compensate Covariate Shift. ACML 2013: 435-450 - [c10]Bastian Bohn, Jochen Garcke, Rodrigo Iza-Teran, Alexander Paprotny, Benjamin Peherstorfer, Ulf Schepsmeier, Clemens-August Thole:
Analysis of Car Crash Simulation Data with Nonlinear Machine Learning Methods. ICCS 2013: 621-630 - 2012
- [c9]Alexander Paprotny, Jochen Garcke:
On a Connection between Maximum Variance Unfolding, Shortest Path Problems and IsoMap. AISTATS 2012: 859-867 - 2010
- [c8]Jochen Garcke:
Classification with Sums of Separable Functions. ECML/PKDD (1) 2010: 458-473
2000 – 2009
- 2009
- [j4]Jochen Garcke, Markus Hegland:
Fitting multidimensional data using gradient penalties and the sparse grid combination technique. Computing 84(1-2): 1-25 (2009) - [j3]Gregory Beylkin, Jochen Garcke, Martin J. Mohlenkamp:
Multivariate Regression and Machine Learning with Sums of Separable Functions. SIAM J. Sci. Comput. 31(3): 1840-1857 (2009) - [c7]Jochen Garcke:
A Dimension Adaptive Combination Technique Using Localised Adaptation Criteria. HPSC 2009: 115-125 - 2007
- [c6]Steffen Börm, Jochen Garcke:
Approximating Gaussian Processes with H2-Matrices. ECML 2007: 42-53 - 2006
- [c5]Jochen Garcke, Markus Hegland:
Fitting Multidimensional Data Using Gradient Penalties and Combination Techniques. HPSC 2006: 235-248 - [c4]Jochen Garcke:
Regression with the optimised combination technique. ICML 2006: 321-328 - 2004
- [b1]Jochen Garcke:
Maschinelles Lernen durch Funktionsrekonstruktion mit verallgemeinerten dünnen Gittern. University of Bonn, Germany, 2004, pp. 1-140 - 2003
- [c3]Jochen Garcke, Markus Hegland, Ole Møller Nielsen:
Parallelisation of Sparse Grids for Large Scale Data Analysis. International Conference on Computational Science 2003: 683-692 - 2002
- [j2]Jochen Garcke, Michael Griebel:
Classification with sparse grids using simplicial basis functions. Intell. Data Anal. 6(6): 483-502 (2002) - 2001
- [j1]Jochen Garcke, Michael Griebel, M. Thess:
Data Mining with Sparse Grids. Computing 67(3): 225-253 (2001) - [c2]Jochen Garcke, Michael Griebel:
Data mining with sparse grids using simplicial basis functions. KDD 2001: 87-96 - [c1]Jochen Garcke, Michael Griebel:
On the Parallelization of the Sparse Grid Approach for Data Mining. LSSC 2001: 22-32
Coauthor Index
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