MCML - Software Packages

Publications

2023

[60]

Glot500. GitHub.

A. Imani, P. Lin, A. H. Kargaran, S. Severini, M. J. Sabet, N. Kassner, C. Ma, H. Schmid, A. F. T. Martins, F. Yvon and H. Schütze.
Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages.
In Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics (ACL 2023). Toronto, Canada, 2023-07-09/2023-07-14.
DOI.
[59]

InstanceFormer. GitHub.

R. Koner, T. Hannan, S. Shit, S. Sharifzadeh, M. Schubert, T. Seidl and V. Tresp.
InstanceFormer: An Online Video Instance Segmentation Framework.
37th Conference on Artificial Intelligence (AAAI 2023). Washington, DC, USA, 2023-02-07/2023-02-14.
DOI.
[58]

MeVTR. GitHub.

G. Zhang, J. Ren, J. Gu and V. Tresp.
Multi-event Video-Text Retrieval.
IEEE/CVF International Conference on Computer Vision (ICCV 2023). Paris, France, 2023-10-02/2023-10-06.
PDF.


2022

[57]

StarQE. GitHub.

D. Alivanistos, M. Berrendorf, M. Cochez and M. Galkin.
Query Embedding on Hyper-Relational Knowledge Graphs.
10th International Conference on Learning Representations (ICLR 2022). Virtual, 2022-04-25/2022-04-29.
URL.
[56]

robust_object_detection. GitHub.

M. Bernhard and M. Schubert.
Robust Object Detection in Remote Sensing Imagery with Noisy and Sparse Geo-Annotations.
30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL 2022). Seattle, WA, USA, 2022-11-01/2022-11-04.
DOI.
[55]

ilpc2022. GitHub.

M. Galkin, M. Berrendorf and C. T. Hoyt.
An Open Challenge for Inductive Link Prediction on Knowledge Graphs.
Workshop on Graph Learning Benchmarks (GLB 2022) at the International World Wide Web Conference (WWW 2022). Virtual, 2022-04-22/2022-04-29.
arXiv.
[54]

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.
6th International Workshop on Interactive Adaptive Learning (IAL 2022) co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2022). Grenoble, France, 2022-09-19/2022-09-22.
PDF.
[53]

VERIPS. GitHub.

S. Gilhuber, P. Jahn, Y. Ma and T. Seidl.
Verips: Verified Pseudo-label Selection for Deep Active Learning.
22nd IEEE International Conference on Data Mining (ICDM 2022). Orlando, FL, USA, 2022-11-30/2022-12-02.
DOI.
[52]

tidyfun/tf. GitHub.

J. Goldsmith and F. Scheipl.
tf: S3 classes and methods for tidy functional data. R package.
2022.
[51]

tidyfun. GitHub.

J. Goldsmith and F. Scheipl.
tidyfun: Clean, wholesome, tidy fun with functional data in R. R package.
2022.
[50]

SCAR. GitHub.

E. Hohma, C. Frey, A. Beer and T. Seidl.
SCAR - Spectral Clustering Accelerated and Robustified.
48th International Conference on Very Large Databases (VLDB 2022). Sydney, Australia (and hybrid), 2022-09-05/2022-09-09.
DOI.
[49]

yahpo_gym. GitHub.

F. Pfisterer, L. Schneider, Moosbauer, M. Binder and B. Bischl.
YAHPO Gym - Design Criteria and a new Multifidelity Benchmark for Hyperparameter Optimization.
1st International Conference on Automated Machine Learning (AutoML 2022) co-located with the 39th International Conference on Machine Learning (ICML 2022). Baltimore, MD, USA, 2022-07-25/2022-07-27.
URL.
[48]

sm-comb. GitHub.

P. Roetzer, P. Swoboda, D. Cremers and F. Bernard.
A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matching.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022-06-19/2022-06-24.
DOI.
[47]

latent-diffusion. GitHub.

R. Rombach, A. Blattmann, D. Lorenz, P. Esser and B. Ommer.
High-Resolution Image Synthesis with Latent Diffusion Models.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA, 2022-06-19/2022-06-24.
DOI.
[46]

relationformer. GitHub.

S. Shit, R. Koner, B. Wittmann, J. Paetzold, I. Ezhov, H. Li, J. Pan, S. Sharifzadeh, G. Kaissis, V. Tresp and B. Menze.
Relationformer: A Unified Framework for Image-to-Graph Generation.
17th European Conference on Computer Vision (ECCV 2022). Tel Aviv, Israel, 2022-10-23/2022-10-27.
DOI.
[45]

ambusim-5238. GitHub.

N. Strauss, M. Berrendorf, T. Haider and M. Schubert.
A Comparison of Ambulance Redeployment Systems on Real-World Data.
IEEE International Conference on Data Mining Workshops (ICDMW 2022). Orlando, FL, USA, 2022-11-30/2022-12-02.
DOI.
[44]

PracTools. URL.

R. Valliant, J. A. Dever, F. Kreuter and M. R. Valliant.
Package ‘PracTools’.
2022.
[43]

art-fid. GitHub.

M. Wright and B. Ommer.
ArtFID: Quantitative Evaluation of Neural Style Transfer.
German Conference on Pattern Recognition (DAGM-GCPR 2022). Konstanz, Germany, 2022-09-19/2021-09-22.
DOI.


2021

[42]

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.
20th International Semantic Web Conference (ISWC 2021). Virtual, 2021-10-24/2021-10-28.
DOI.
[41]

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.
[40]

4D-PLS. GitHub.

M. Aygun, A. Osep, M. Weber, M. Maximov, C. Stachniss, J. Behley and L. Leal-Taixé.
4D Panoptic LiDAR Segmentation.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021-06-19/2021-06-25.
DOI.
[39]

mlr3hyperband. URL. GitHub.

M. Becker, S. Gruber, J. Richter, J. Moosbauer and B. Bischl.
mlr3hyperband: Hyperband for 'mlr3'.
2021.
[38]

mlr3tuning. URL. GitHub.

M. Becker, M. Lang, J. Richter, B. Bischl and D. Schalk.
mlr3tuning: Tuning for 'mlr3'.
2021.
[37]

bbotk. URL. GitHub.

M. Becker, J. Richter, M. Lang, B. Bischl and M. Binder.
bbotk: Black-Box Optimization Toolkit.
2021.
[36]

ea-active-learning. GitHub.

M. Berrendorf, E. Faerman and V. Tresp.
Active Learning for Entity Alignment.
43rd European Conference on Information Retrieval (ECIR 2021). Virtual, 2021-03-28/2021-04-01.
DOI.
[35]

ea-sota-comparison. GitHub.

M. Berrendorf, L. Wacker and E. Faerman.
A Critical Assessment of State-of-the-Art in Entity Alignment.
43rd European Conference on Information Retrieval (ECIR 2021). Virtual, 2021-03-28/2021-04-01.
DOI.
[34]

mlrintermbo. URL. GitHub.

M. Binder.
mlrintermbo: Model-Based Optimization for 'mlr3' through 'mlrMBO'.
2021.
[33]

panoptic-reconstruction. GitHub.

M. Dahnert, J. Hou, M. Niessner and A. Dai.
Panoptic 3D Scene Reconstruction From a Single RGB Image.
35th Conference on Neural Information Processing Systems (NeurIPS 2021). Virtual, 2021-12-06/2021-12-14.
PDF.
[32]

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.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021-06-19/2021-06-25.
DOI.
[31]

ecir2021-am-search. GitHub.

M. Fromm, M. Berrendorf, S. Obermeier, T. Seidl and E. Faerman.
Diversity Aware Relevance Learning for Argument Search.
43rd European Conference on Information Retrieval (ECIR 2021). Virtual, 2021-03-28/2021-04-01.
arXiv.
[30]

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.
35th Conference on Artificial Intelligence (AAAI 2021). Virtual, 2021-02-02/2021-02-09.
DOI.
[29]

IsoMuSh. GitHub.

M. Gao, Z. Lähner, J. Thunberg, D. Cremers and F. Bernard.
Isometric Multi-Shape Matching.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Virtual, 2021-06-19/2021-06-25.
DOI.
[28]

tandem. GitHub.

L. Koestler, N. Yang, N. Zeller and D. Cremers.
TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo.
Conference on Robot Learning (CoRL 2021). London, UK, 2021-11-08/2021-11-11.
PDF.
[27]

mlr3measures. URL.

M. Lang.
mlr3measures: Performance Measures for 'mlr3'.
2021.
[26]

paradox. URL. GitHub.

M. Lang, B. Bischl, J. Richter, X. Sun and M. Binder.
paradox: Define and Work with Parameter Spaces for Complex Algorithms.
2021.
[25]

paper_2021_xautoml. GitHub.

J. Moosbauer, J. Herbinger, G. Casalicchio, M. Lindauer and B. Bischl.
Explaining Hyperparameter Optimization via Partial Dependence Plots.
35th Conference on Neural Information Processing Systems (NeurIPS 2021). Virtual, 2021-12-06/2021-12-14.
PDF.
[24]

deepregression. GitHub.

D. Rügamer, F. Pfisterer and P. Baumann.
deepregression: Fitting Semi-Structured Deep Distributional Regression in R.
2021.
[23]

mlr3spatiotempcv. URL.

P. Schratz and M. Becker.
mlr3spatiotempcv: Spatiotemporal Resampling Methods for 'mlr3'.
2021.
[22]

registr 2.0. GitHub.

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


2020

[21]

pykeen/benchmarking. DOI.

M. Ali, C. T. Hoyt, L. Vermue, M. Galkin and M. Berrendorf.
pykeen/benchmarking. Version v1.0.
2020.
[20]

mlr3fselect. URL.

M. Becker, P. Schratz, M. Lang and B. Bischl.
mlr3fselect: Feature Selection for 'mlr3'.
2020.
[19]

mberr/ea-active-learning. DOI.

M. Berrendorf and E. Faerman.
mberr/ea-active-learning: Zenodo. Version 1.0.1.
2020.
[18]

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.
42nd European Conference on Information Retrieval (ECIR 2020). Virtual, 2020-04-14/2020-04-17.
DOI.
[17]

ea-sota-comparison. DOI.

M. Berrendorf, L. Wacker and E. Faerman.
mberr/ea-sota-comparison: Zenodo. Version v1.1.1.
2020.
[16]

mlr3pipelines. URL. GitHub.

M. Binder, F. Pfisterer, L. Schneider, B. Bischl, M. Lang and S. Dandl.
mlr3pipelines: Preprocessing Operators and Pipelines for 'mlr3'.
2020.
[15]

sscn. GitHub.

J. Busch, E. Faerman, M. Schubert and T. Seidl.
Learning Self-Expression Metrics for Scalable and Inductive Subspace Clustering.
Workshop on Self-Supervised Learning - Theory and Practice (SSL 2020) at the 34th Conference on Neural Information Processing Systems (NeurIPS 2020). Virtual, 2020-12-06/2020-12-12.
arXiv.
[14]

fda-ndr. GitHub.

M. Herrmann.
fda-ndr: Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction. R package.
2020.
[13]

manifun. GitHub.

M. Herrmann.
manifun: Collection of functions to work with embeddings and functional data. R package.
2020.
[12]

mlr3db. URL. GitHub.

M. Lang.
mlr3db: Data Base Backend for 'mlr3'.
2020.
[11]

mlr3oml. URL. GitHub.

M. Lang.
mlr3oml: Connector Between 'mlr3' and 'OpenML'.
2020.
[10]

mlr3learners. URL. GitHub.

M. Lang, Q. Au, S. Coors and P. Schratz.
mlr3learners: Recommended Learners for 'mlr3'.
2020.
[9]

mlr3viz. URL. GitHub.

M. Lang, P. Schratz and R. Sonabend.
mlr3viz: Visualizations for 'mlr3'.
2020.
[8]

medil. GitHub.

A. Markham, A. Chivukula and M. Grosse-Wentrup.
MeDIL: A Python Package for Causal Modelling.
10th International Conference on Probabilistic Graphical Models (PGM 2020). Aalborg, Denmark, 2020-09-23/2020-09-25.
URL.
[7]

mlr3cluster. URL. GitHub.

D. Pulatov and M. Lang.
mlr3cluster: Cluster Extension for 'mlr3'.
2020.
[6]

tidyfun. URL. GitHub.

F. Scheipl, J. Goldsmith and J. Wrobel.
tidyfun: Tools for Tidy Functional Data. R package.
2020.
[5]

mlr3filters. URL. GitHub.

P. Schratz, M. Lang, B. Bischl and M. Binder.
mlr3filters: Filter Based Feature Selection for 'mlr3'.
2020.
[4]

mlr3proba. DOI. URL.

R. Sonabend, F. Kiraly and M. Lang.
mlr3proba: Probabilistic Supervised Learning for 'mlr3'. R package version 0.2.6.
2020.
[3]

registr. 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. GitHub.

M. Binder, S. Dandl and J. Moosbauer.
mosmafs: Multi-Objective Simultaneous Model and Feature Selection. R package.
2019.
[1]

refund. URL.

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.