MCML - Software Packages

2023

[52]

InstanceFormer. GitHub.

R. Koner, T. Hannan, S. Shit, S. Sharifzadeh, M. Schubert, T. Seidl and V. Tresp.
InstanceFormer: An online Video Instance Segmentation Framework.
In Proceedings of the 37th Conference on Artificial Intelligence (AAAI 2023). Washington, DC, USA, 2023.
arXiv.

2022

[51]

StarQE. GitHub.

D. Alivanistos, M. Berrendorf, M. Cochez and M. Galkin.
Query Embedding on Hyper-Relational Knowledge Graphs.
In Proceedings of the 10th International Conference on Learning Representations (ICLR 2022). Virtual, 2022.
PDF.

[50]

robust_object_detection. GitHub.

M. Bernhard and M. Schubert.
Robust Object Detection in Remote Sensing Imagery with Noisy and Sparse Geo-Annotations.
In Proceedings of the 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2022). Seattle, WA, USA, 2022.

[49]

ilpc2022. GitHub.

M. Galkin, M. Berrendorf and C. T. Hoyt.
An Open Challenge for Inductive Link Prediction on Knowledge Graphs.
In Proceedings of the 2022 Workshop on Graph Learning Benchmarks (GLB 2022) at the International World Wide Web Conference (WWW 2022). Virtual, 2022.
arXiv.

[48]

low-dim-div-sampling. GitHub.

S. Gilhuber, M. Berrendorf, Y. Ma and T. Seidl.
Accelerating Diversity Sampling for Deep Active Learning By Low-Dimensional Representations.
In Proceedings of the 6th International Workshop on Interactive Adaptive Learning (IAL2022) co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022). Grenoble, France, 2022.

[47]

VERIPS. GitHub.

S. Gilhuber, P. Jahn, Y. Ma and T. Seidl.
Verips: Verified Pseudo-label Selection for Deep Active Learning.
In Proceedings of the 22nd IEEE International Conference on Data Mining (ICDM 2022). Orlando, FL, USA, 2022.

[46]

sm-comb. GitHub.

P. Roetzer, P. Swoboda, D. Cremers and F. Bernard.
A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matching.
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022.
DOI.

[45]

latent-diffusion. GitHub.

R. Rombach, A. Blattmann, D. Lorenz, P. Esser and B. Ommer.
High-Resolution Image Synthesis with Latent Diffusion Models.
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022.
arXiv.

[44]

ambusim-5238. GitHub.

N. Strauss, M. Berrendorf, T. Haider and M. Schubert.
A Comparison of Ambulance Redeployment Systems on Real-World Data.
In Proceedings of the 1st Workshop on Urban Internet-of-Things Intelligence (UNIT 2022) co-located with the 22nd IEEE International Conference on Data Mining (ICDM 2022). Orlando, FL, USA, 2022.

[43]

Package ‘PracTools’. R-package.

R. Valliant, J. A. Dever, F. Kreuter and M. R. Valliant.
Package ‘PracTools’.
2022.

[42]

art-fid. GitHub.

M. Wright and B. Ommer.
ArtFID: Quantitative Evaluation of Neural Style Transfer.
In Proceedings of the German Conference on Pattern Recognition (GCPR 2022). Konstanz, Germany, 2022.
DOI.

2021

[41]

hyper_relational_ilp. GitHub.

M. Ali, M. Berrendorf, M. Galkin, V. Thost, T. Ma, V. Tresp and J. Lehmann.
Improving Inductive Link Prediction Using Hyper-Relational Facts.
In Proceedings of the 20th International Semantic Web Conference (ISWC 2021). Virtual, 2021.
DOI.

[40]

pykeen/benchmarking. GitHub.

M. Ali, M. Berrendorf, C. T. Hoyt, L. Vermue, M. Galkin, S. Sharifzadeh, A. Fischer, V. Tresp and J. Lehmann.
Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models under a Unified Framework.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2021).
DOI.

[39]

4D-PLS. GitHub.

M. Aygun, A. Osep, M. Weber, M. Maximov, C. Stachniss, J. Behley and L. Leal-Taixé.
4D Panoptic LiDAR Segmentation.
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021.

[38]

mlr3hyperband: Hyperband for 'mlr3'. MLR. GitHub.

M. Becker, S. Gruber, J. Richter, J. Moosbauer and B. Bischl.
mlr3hyperband: Hyperband for 'mlr3'.
2021.

[37]

mlr3tuning: Tuning for 'mlr3'. MLR. GitHub.

M. Becker, M. Lang, J. Richter, B. Bischl and D. Schalk.
mlr3tuning: Tuning for 'mlr3'.
2021.

[36]

bbotk: Black-Box Optimization Toolkit. MLR. GitHub.

M. Becker, J. Richter, M. Lang, B. Bischl and M. Binder.
bbotk: Black-Box Optimization Toolkit.
2021.

[35]

ea-active-learning. GitHub.

M. Berrendorf, E. Faerman and V. Tresp.
Active Learning for Entity Alignment.
In Proceedings of the 43rd European Conference on Information Retrieval (ECIR 2021). Virtual, 2021.
DOI.

[34]

ea-sota-comparison. GitHub.

M. Berrendorf, L. Wacker and E. Faerman.
A Critical Assessment of State-of-the-Art in Entity Alignment.
In Proceedings of the 43rd European Conference on Information Retrieval (ECIR 2021). Virtual, 2021.
DOI.

[33]

mlrintermbo: Model-Based Optimization for 'mlr3' through 'mlrMBO'. R-package. GitHub.

M. Binder.
mlrintermbo: Model-Based Optimization for 'mlr3' through 'mlrMBO'.
2021.

[32]

panoptic-reconstruction. GitHub.

M. Dahnert, J. Hou, M. Niessner and A. Dai.
Panoptic 3D Scene Reconstruction From a Single RGB Image.
In Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021). Virtual, 2021.
PDF.

[31]

neuromorph. GitHub.

M. Eisenberger, D. Novotny, G. Kerchenbaum, P. Labatut, N. Neverova, D. Cremers and A. Vedaldi.
NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go.
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021.

[30]

ecir2021-am-search. GitHub.

M. Fromm, M. Berrendorf, S. Obermeier, T. Seidl and E. Faerman.
Diversity Aware Relevance Learning for Argument Search.
In Proceedings of the 43rd European Conference on Information Retrieval (ECIR 2021). Virtual, 2021.
arXiv.

[29]

aaai2021-am-peer-reviews. GitHub.

M. Fromm, E. Faerman, M. Berrendorf, S. Bhargava, R. Qi, Y. Zhang, L. Dennert, S. Selle, Y. Mao and T. Seidl.
Argument Mining Driven Analysis of Peer-Reviews.
In Proceedings of the 35th Conference on Artificial Intelligence (AAAI 2021). Virtual, 2021.
arXiv.

[28]

IsoMuSh. GitHub.

M. Gao, Z. Lähner, J. Thunberg, D. Cremers and F. Bernard.
Isometric Multi-Shape Matching.
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021.

[27]

tandem. GitHub.

L. Koestler, N. Yang, N. Zeller and D. Cremers.
TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo.
In Proceedings of the Conference on Robot Learning (CoRL 2021). London, UK, 2021.
PDF.

[26]

mlr3measures: Performance Measures for 'mlr3'. R-package.

M. Lang.
mlr3measures: Performance Measures for 'mlr3'.
2021.

[25]

paradox: Define and Work with Parameter Spaces for Complex Algorithms. MLR. GitHub.

M. Lang, B. Bischl, J. Richter, X. Sun and M. Binder.
paradox: Define and Work with Parameter Spaces for Complex Algorithms.
2021.

[24]

paper_2021_xautoml. GitHub.

J. Moosbauer, J. Herbinger, G. Casalicchio, M. Lindauer and B. Bischl.
Explaining Hyperparameter Optimization via Partial Dependence Plots (Poster).
In Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021). Virtual, 2021.
PDF.

[23]

deepregression: Fitting Semi-Structured Deep Distributional Regression in R. GitHub.

D. Rügamer, F. Pfisterer and P. Baumann.
deepregression: Fitting Semi-Structured Deep Distributional Regression in R.
2021.

[22]

mlr3spatiotempcv: Spatiotemporal Resampling Methods for 'mlr3'. R-package.

P. Schratz and M. Becker.
mlr3spatiotempcv: Spatiotemporal Resampling Methods for 'mlr3'.
2021.

[21]

registr. GitHub.

J. Wrobel and A. Bauer.
registr 2.0: Incomplete Curve Registration for Exponential Family Functional Data.
The Journal of Open Source Software 6.61 (2021).
DOI.

2020

[20]

pykeen/benchmarking. Version v1.0. DOI.

M. Ali, C. T. Hoyt, L. Vermue, M. Galkin and M. Berrendorf.
pykeen/benchmarking. Version v1.0.
2020.

[19]

mlr3fselect: Feature Selection for 'mlr3'. R-package.

M. Becker, P. Schratz, M. Lang and B. Bischl.
mlr3fselect: Feature Selection for 'mlr3'.
2020.

[18]

mberr/ea-active-learning: Zenodo. Version 1.0.1. DOI.

M. Berrendorf and E. Faerman.
mberr/ea-active-learning: Zenodo. Version 1.0.1.
2020.

[17]

kg-alignment-lessons-learned. GitHub.

M. Berrendorf, E. Faerman, V. Melnychuk, V. Tresp and T. Seidl.
Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned.
In Proceedings of the 42nd European Conference on Information Retrieval (ECIR 2020). Virtual, 2020.
DOI.

[16]

mberr/ea-sota-comparison: Zenodo. Version v1.1.1. DOI.

M. Berrendorf, L. Wacker and E. Faerman.
mberr/ea-sota-comparison: Zenodo. Version v1.1.1.
2020.

[15]

mlr3pipelines: Preprocessing Operators and Pipelines for 'mlr3'. MLR. GitHub.

M. Binder, F. Pfisterer, L. Schneider, B. Bischl, M. Lang and S. Dandl.
mlr3pipelines: Preprocessing Operators and Pipelines for 'mlr3'.
2020.

[14]

fda-ndr: Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction. R package. GitHub.

M. Herrmann.
fda-ndr: Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction. R package.
2020.

[13]

manifun: Collection of functions to work with embeddings and functional data. R package.

M. Herrmann.
manifun: Collection of functions to work with embeddings and functional data. R package.
2020.

[12]

mlr3db: Data Base Backend for 'mlr3'. MLR. GitHub.

M. Lang.
mlr3db: Data Base Backend for 'mlr3'.
2020.

[11]

mlr3oml: Connector Between 'mlr3' and 'OpenML'. MLR. GitHub.

M. Lang.
mlr3oml: Connector Between 'mlr3' and 'OpenML'.
2020.

[10]

mlr3learners: Recommended Learners for 'mlr3'. MLR. GitHub.

M. Lang, Q. Au, S. Coors and P. Schratz.
mlr3learners: Recommended Learners for 'mlr3'.
2020.

[9]

mlr3viz: Visualizations for 'mlr3'. MLR. GitHub.

M. Lang, P. Schratz and R. Sonabend.
mlr3viz: Visualizations for 'mlr3'.
2020.

[8]

medil.

A. Markham, A. Chivukula and M. Grosse-Wentrup.
MeDIL: A Python Package for Causal Modelling.
In Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020). Aalborg, Denmark, 2020.
GitLab.

[7]

mlr3cluster: Cluster Extension for 'mlr3'. MLR. GitHub.

D. Pulatov and M. Lang.
mlr3cluster: Cluster Extension for 'mlr3'.
2020.

[6]

tidyfun: Tools for Tidy Functional Data. R package. tidyfun:Tools for Tidy Functional Dat. GitHub.

F. Scheipl, J. Goldsmith and J. Wrobel.
tidyfun: Tools for Tidy Functional Data. R package.
2020.

[5]

mlr3filters: Filter Based Feature Selection for 'mlr3'. MLR. GitHub.

P. Schratz, M. Lang, B. Bischl and M. Binder.
mlr3filters: Filter Based Feature Selection for 'mlr3'.
2020.

[4]

mlr3proba: Probabilistic Supervised Learning for 'mlr3'. R package version 0.2.6. DOI. R-package.

R. Sonabend, F. Kiraly and M. Lang.
mlr3proba: Probabilistic Supervised Learning for 'mlr3'. R package version 0.2.6.
2020.

[3]

registr: Curve Registration for Exponential Family Functional Data. R package. GitHub.

J. Wrobel, A. Bauer, J. Goldsmith, E. McDonnel and F. Scheipl.
registr: Curve Registration for Exponential Family Functional Data. R package.
2020.

2019

[2]

mosmafs: Multi-Objective Simultaneous Model and Feature Selection (R package). GitHub.

M. Binder, S. Dandl and J. Moosbauer.
mosmafs: Multi-Objective Simultaneous Model and Feature Selection (R package).
2019.

[1]

refund: Regression with Functional Data. R-package.

J. Goldsmith, F. Scheipl, L. Huang, J. Wrobel, C. Di, J. Gellar, J. Harezlak, M. W. McLean, B. Swihart, L. Xiao, C. Crainiceanu and P. T. Reiss.
refund: Regression with Functional Data.
2019.