Machine Learning

Mitarbeiter: Jörn Ostermann, Bodo Rosenhahn, Florian Kluger, Daniel Gritzner
Einleitung

Am Institut für Informationsverarbeitung (TNT) werden Methoden im breiten Feld des Machine Learning entwickelt und auf verschiedene Problemstellungen angewandt. Das Ziel ist es, automatisiert Wissen und semantische Zusammenhänge aus großen Datenmengen zu extrahieren. Diese Informationen sind für Anwendungen wie z.B. autonomes Fahren, Krebsdiagnose, Luftbildauswertung, Augmented Reality und Industrie 4.0 extrem wichtig.

 

Aktuelle Forschungsthemen

Autonomes Fahren:

Autonome Fahrzeuge erhöhen die Sicherheit aller Teilnehmer im Straßenverkehr, indem fehlbare menschliche Fahrer durch zuverlässige Algorithmen ersetzt werden. Dazu sind robuste Methoden zur semantischen Analyse der Fahrumgebung notwendig, auf deren Basis Gefahrensituationen erkannt und Entscheidungen getroffen werden. Mithilfe maschineller Lernverfahren entwickelt das TNT Algorithmen, welche Daten unterschiedlicher Sensoren – z.B. Kameras oder Lidar – analysieren, um sicheres autonomes Fahren zu ermöglichen.

Bildsynthese mit Neuronalen Netzen:

Mit gegeneinander arbeitenden Netzwerken, sogenannten Adversarial Neworks, ist es möglich, unterschiedliche Aufgaben wie die Generierung realistisch wirkender Bilder und den Informations-transfer zwischen unterschiedlichen Domänen und Sensoren zu bewerkstelligen. So kann beispielsweise die Auflösung eines Bildes erhöht oder fehlende bzw. verdeckte Bildbereiche rekonstruiert werden.

Einsatz von Low-Cost-Sensoren:

Teilnehmer im alltäglichen Straßenverkehr können mit Hilfe von Low-Cost-Sensoren in kurzer Zeit große Mengen interessanter Daten sammeln und zugänglich machen. Daraus werden mit grafischen Modellen und spezialisierten Neuronalen Netzen semantische Informationen extrahiert, wie z.B. Baustellen, welche die Bewegungsfreiheit einschränken, oder Stoßzeiten im Verkehr.

Videospiel AI:

Videospiele stellen vielfältige, aber gleichzeitig gut kontrollierbare Umgebungen zur Erforschung von Algorithmen zur Entscheidungsplanung dar. Beispielsweise werden mit Reinforcement Learning intelligente Agenten für Videospiele entwickelt, welche die gleichen Informationen und Möglichkeiten wie echte Spieler haben.

Luftbildauswertung:

Aus Luftbildern lassen sich viele interessante Informationen extrahieren, z.B. aktuelle Karten, Städtewachstum über die Zeit, die Beurteilung von Verkehrsaufkommen oder der Auslastung von Parkplätzen. Das TNT setzt Deep Learning ein, um automatisch sowohl die Art der Nutzung von Flächen zu ermitteln, als auch Objekte in Luftbildern zu erkennen. EOT;

Verwendete Methoden

Neuronale Netze (CNNs, Autoencoder, RNNs, GANs, …) , Statistische Lernverfahren (Random Forest, Gaussian Mixture Models, Hidden Markov Models, …) , Bestärkendes Lernen (Q-Learning, MCTS, …)

 

 

  • Conference Contributions
    • Yuren Cong, Mengmeng Xu, Christian Simon, Shoufa Chen, Jiawei Ren, Yanping Xie, Juan-Manuel Perez-Rua, Bodo Rosenhahn, Tao Xiang, Sen He
      FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing
      International Conference on Learning Representations (ICLR) , 2024
    • Shoufa Chen, Mengmeng Xu, Jiawei Ren, Yuren Cong, Sen He, Yanping Xie, Animesh Sinha, Ping Luo, Tao Xiang, Juan-Manuel Perez-Rua
      GenTron: Delving Deep into Diffusion Transformers for Image and Video Generation
      Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
    • Yeremia Gunawan Adhisantoso, Jan Voges, Jörn Ostermann
      PEKORA: High-Performance 3D Genome Reconstruction Using K-th Order Spearman's Rank Correlation Approximation
      ISMB/ECCB 2023, Lyon (FR), July 2023
    • Fabian Müntefering, Jörn Ostermann, Jan Voges
      BACON: Bacterial Clone Recognition from Metagenomic Sequencing Data
      AICPM 2023, Hannover (DE), September 2023
    • Christian Rohlfing, Thibaut Meyer, Jens Schneider, Jan Voges
      Python Wrapper for Context-based Adaptive Binary Arithmetic Coding
      2023 IEEE International Conference on Visual Communications and Image Processing (VCIP), December 2023
    • Yuren Cong, Jinhui Yi, Bodo Rosenhahn, Michael Yang
      SSGVS: Semantic Scene Graph-to-Video Synthesis
      Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023
    • Carl Hvarfner, Danny Stoll, Artur Souza, Marius Lindauer, Frank Hutter, Luigi Nardi
      piBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization
      10th International Conference on Learning Representations, ICLR'22, OpenReview, pp. 1-30, April 2022
    • André Biedenkapp, David Speck, Silvan Sievers, Frank Hutter, Marius Lindauer, Jendrik Seipp
      Learning Domain-Independent Policies for Open List Selection
      Proceedings of the 3rd ICAPS workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL), pp. 1-9, 2022
    • Quy Le Xuan, Yeremia Gunawan Adhisantoso, Marco Munderloh, Jörn Ostermann
      Uncertainty-Aware Remaining Useful Life Prediction for Predictive Maintenance Using Deep Learning
      16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME, 2022
    • Yeremia Gunawan Adhisantoso, Quy Le Xuan, Christoph Kellerman, Marco Munderloh, Jörn Ostermann
      Introduction to Deep Degradation Metric in Smart Production Ecosystems
      16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME, 2022
    • Sen He, Wentong Liao, Michael Ying Yang, Yongxin Yang, Yi-Zhe Song, Bodo Rosenhahn, Tao Xiang
      Context-Aware Layout to Image Generation with Enhanced Object Appearance
      IEEE Conference on Computer Vision and Pattern Recognition, June 2021
    • Cheng Hao, Wentong Liao, Xuejiao Tang, Michael Ying Yang, Monika Sester, Bodo Rosenhahn
      Exploring Dynamic Context for Multi-path Trajectory Prediction
      International Conference on Robotics and Automation , May 2021
    • Wentong Liao, Cuiling Lan, Michael Ying Yang, Wenjung Zeng, Bodo Rosenhahn
      Target-Tailored Source-Transformation for Scene Graph Generation
      In CVPR Workshop on Multi-Sensor Fusion for Dynamic Scene Understanding, June 2021
    • Yuren Cong, Wentong Liao, Hanno Ackermann, Michael Yang Yang, Bodo Rosenhahn
      Spatial-Temporal Transformer for Dynamic Scene Graph Generation
      International Conference on Computer Vision (ICCV), July 2021
    • David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller, Marius Lindauer
      Learning Heuristic Selection with Dynamic Algorithm Configuration
      Proceedings of the 31st International Conference on Automated Planning and Scheduling {(ICAPS'21)}, August 2021
    • Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer
      DACBench: A Benchmark Library for Dynamic Algorithm Configuration
      Proceedings of the international joint conference on artificial intelligence (IJCAI), August 2021
    • Julia Guerrero-Viu, Sven Hauns, Sergio Izquierdo, Guilherme Miotto, Simon Schrodi, Andre Biedenkapp, Thomas Elsken, Difan Deng, Marius Lindauer, Frank Hutter
      Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization
      Proceedings of the international workshop on Automated Machine Learning (AutoML) at ICML'21, July 2021
    • Andre Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer
      TempoRL: Learning When to Act
      Proceedings of the international conference on machine learning (ICML), July 2021
    • Theresa Eimer, Andre Biedenkapp, Frank Hutter, Marius Lindauer
      Self-Paced Context Evaluation for Contextual Reinforcement Learning
      Proceedings of the international conference on machine learning (ICML), July 2021
    • Artur Souza, Luigi Nardi, Leonardo Oliveira, Kunle Olukotun, Marius Lindauer, Frank Hutter
      Bayesian Optimization with a Prior for the Optimum
      Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), September 2021
    • Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl
      Towards Explaining Hyperparameter Optimization via Partial Dependence Plots
      Proceedings of the international workshop on Automated Machine Learning (AutoML) at ICML'21, July 2021
    • Katharina Eggensperger, Philipp Müller, Neeratyoy Mallik, Matthias Feurer, René Sass, Aaron Klein, Noor Awad, Marius Lindauer, Frank Hutter
      HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO
      Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) (Datasets and Benchmarks Track), December 2021
    • Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl
      Explaining Hyperparameter Optimization via Partial Dependence Plots
      Proceedings of the international conference on Neural Information Processing Systems (NeurIPS), December 2021
    • Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka
      Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data
      Proceedings of the international conference on Neural Information Processing Systems (NeurIPS), December 2021
    • Theresa Eimer, Carolin Benjamins, Marius Lindauer
      Hyperparameters in Contextual RL are Highly Situational
      NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning, December 2021
    • Carolin Benjamins, Theresa Eimer, Frederik Schubert, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer
      CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning
      NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning, December 2021
    • Yuren Cong, Hanno Ackermann, Wentong Liao, Michael Ying Yang, Bodo Rosenhahn
      NODIS: Neural Ordinary Differential Scene Understanding
      European Conference on Computer Vision (ECCV), August 2020
    • He Sen, Liao Wentong, Hamed Rezazadegan Tavakoli, Michael Ying Yang, Bodo Rosenhahn, Nicolas Pugeault
      Image Captioning through Image Transformer
      Asian Conference on Computer Vision (ACCV), IEEE, Kyoto, November 2020
    • Andre Biedenkapp, H. Furkan Bozkurt, Theresa Eimer, Frank Hutter, Marius Lindauer
      Algorithm Control: Foundation of a New Meta-Algorithmic Framework
      Proceedings of the European Conference on Artificial Intelligence (ECAI), 2020
    • David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller, Marius Lindauer
      Learning Heuristic Selection with Dynamic Algorithm Configuration
      Proceedings of international workshop on Bridging the Gap Between AI Planning and Reinforcement Learning at ICAPS, June 2020
    • Theresa Eimer, Andre Biedenkapp, Frank Hutter, Marius Lindauer
      Towards Self-Paced Context Evaluations for Contextual Reinforcement Learning
      Workshop on Inductive Biases, Invariances and Generalization in Reinforcement Learning (BIG@ICML'20), July 2020
    • Artur Souza, Luigi Nardi, Leonardo Oliveira, Kunle Olukotun, Marius Lindauer, Frank Hutter
      Prior-guided Bayesian Optimization
      Proceedings of the Workshop on Meta-Learning (NeurIPS), pp. 1-19, December 2020
    • Gresa Shala, Andre Biedenkapp, Noor Awad, Steven Adriaensen, Marius Lindauer, Frank Hutter
      Learning Step-Size Adaptation in CMA-ES
      Proceedings of the Sixteenth International Conference on Parallel Problem Solving from Nature ({PPSN}'20), September 2020
    • Berend Denkena, Marc Dittrich, Marius Lindauer, Julia Mainka, Lukas Stürenburg
      Using AutoML to Optimize Shape Error Prediction in Milling Processes
      Proceedings of 20th Machining Innovations Conference for Aerospace Industry (MIC), December 2020
    • Idoia Ochoa, Hongyi Li, Florian Baumgarte, Charles Hergenrother, Jan Voges, Mikel Hernaez
      AliCo: A New Efficient Representation for SAM Files
      2019 Data Compression Conference (DCC), pp. 93-102, March 2019
    • Maximilian Benedikt Schier, Niclas Wüstenbecker
      Adversarial N-player Search using Locality for the Game of Battlesnake
      INFORMATIK 2019, September 2019
    • M. Lindauer and M. Feurer and K. Eggensperger and A. Biedenkapp and F. Hutter
      Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters
      {IJCAI} 2019 {DSO} Workshop, August 2019
    • Ana A. Hernandez-Lopez, Jan Voges, Claudio Alberti, Marco Mattavelli, Jörn Ostermann
      Lossy Compression of Quality Scores in Differential Gene Expression: A First Assessment and Impact Analysis
      2018 Data Compression Conference (DCC), pp. 167-176, March 2018
    • Christoph Reinders, Hanno Ackermann, Michael Ying Yang, Bodo Rosenhahn
      Object Recognition from very few Training Examples for Enhancing Bicycle Maps
      2018 IEEE Intelligent Vehicles Symposium (IV), June 2018
    • M. Feurer and K. Eggensperger and S. Falkner and M. Lindauer and F. Hutter
      Practical Automated Machine Learning for the AutoML Challenge 2018
      ICML 2018 AutoML Workshop, July 2018
    • K. Eggensperger and M. Lindauer and F. Hutter
      Neural Networks for Predicting Algorithm Runtime Distributions
      Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’18), pp. 1442-1448, July 2018
    • A. Biedenkapp and J. Marben and M. Lindauer and F. Hutter
      CAVE: Configuration Assessment, Visualization and Evaluation
      Proceedings of the International Conference on Learning and Intelligent Optimization (LION'18), June 2018
    • Wentong Liao, Chun Yang, Michael Ying Yang, Bodo Rosenhahn
      Security Event Recognition for Visual Surveillance
      ISPRS Annals of Photogrammetry, Remote Sensing \& Spatial Information Sciences, Vol. 4, June 2017
    • Ana A. Hernandez-Lopez, Jan Voges, Claudio Alberti, Marco Mattavelli, Jörn Ostermann
      Differential Gene Expression with Lossy Compression of Quality Scores in RNA-Seq Data
      2017 Data Compression Conference (DCC), p. 444, April 2017
    • Florian Kluger, Hanno Ackermann, Michael Ying Yang, Bodo Rosenhahn
      Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection
      39th German Conference on Pattern Recognition, Springer Lecture Notes in Computer Science (LNCS), Basel, Switzerland, September 2017
    • Claudio Alberti, Noah Daniels, Mikel Hernaez, Jan Voges, Rachel L. Goldfeder, Ana A. Hernandez-Lopez, Marco Mattavelli, Bonnie Berger
      An Evaluation Framework for Lossy Compression of Genome Sequencing Quality Values
      2016 Data Compression Conference (DCC), pp. 221-230, March 2016
    • Jan Voges, Marco Munderloh, Jörn Ostermann
      Predictive Coding of Aligned Next-Generation Sequencing Data
      2016 Data Compression Conference (DCC), pp. 241-250, March 2016
    • Wentong Liao, Bodo Rosenhahn, Yang Michael
      Gaussian Process for Activity Modeling and Anomaly Detection
      International Society for Photogrammetry and Remote Sensing ISA workshop, La Grande Motte, France, September 2015
    • Wentong Liao, Yang Michael, Bodo Rosenhahn
      Video Event Recognition by Combining HDP and Gaussian Process
      IEEE International Conference on Computer Vision (ICCV) Workshop, pp. 19-27, Santiago, Chile, December 2015
    • Michael Ying Yang, Yu Qiang, Bodo Rosenhahn
      A global-to-local framework for infrared and visible image sequence registration
      IEEE Winter Conference on Applications of Computer Vision, accpeted for publication, January 2015
    • Michael Ying Yang
      A Generic Probabilistic Graphical Model for Region-based Scene Interpretation
      International Conference on Computer Vision Theory and Applications, accpeted for publication, March 2015
    • Florian Baumann, Liu Wei, Arne Ehlers, Bodo Rosenhahn
      Sequential Boosting for Learning a Random Forest Classifier
      IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Beach, HI, USA, January 2015
    • Florian Baumann, Karsten Vogt, Arne Ehlers, Bodo Rosenhahn
      Probabilistic Nodes for Modelling Classification Uncertainty for Random Forest
      14th IAPR International Conference on Machine Vision Applications (MVA), Tokio, Japan, May 2015
    • Florian Baumann, Jinghui Chen, Karsten Vogt, Bodo Rosenhahn
      Improved Threshold Selection by using Calibrated Probabilities for Random Forest Classifiers
      12th Conference on Computer and Robot Vision (CRV), Halifax, Nova Scotia, Canada, June 2015
    • Christoph Reinders, Florian Baumann, Björn Scheuermann, Arne Ehlers, Nicole Mühlpforte, Alfred O. Effenberg, Bodo Rosenhahn
      On-The-Fly Handwriting Recognition using a High-Level Representation
      The 16th International Conference on Computer Analysis of Images and Patterns (CAIP), Valetta, Malta, September 2015
    • Michele Fenzi, Nico Mentzer, Guillermo Payá-Vayá, Tu Ngoc Nguyen, Thomas Risse, Holger Blume, Jörn Ostermann
      Automatic Situation Assessment for Event-driven Video Analysis
      IEEE International Conference on Advanced Video Signal-Based Surveillance (AVSS), accepted as oral presentation , Seoul, South Korea, August 2014
    • Michael Ying Yang, Bodo Rosenhahn
      Video Segmentation with Joint Object and Trajectory Labeling
      IEEE Winter Conference on Applications of Computer Vision, IEEE, March 2014
    • Michael Ying Yang, Sitong Feng, Bodo Rosenhahn
      Sparse optimization for motion segmentation
      ACCV Workshop on Video Segmentation in Computer Vision, November 2014
    • Florian Baumann, Jie Liao, Arne Ehlers, Bodo Rosenhahn
      Motion Binary Patterns for Action Recognition
      3rd International Conference on Pattern Recognition Applications and Methods, France, Angers Loire Valley, March 2014
    • Florian Baumann, Jie Liao, Arne Ehlers, Bodo Rosenhahn
      Computation Strategies for Volume Local Binary Patterns applied to Action Recognition
      11th IEEE International Conference on Advanced Video and Signal-Based Surveillance , Seoul, Korea, August 2014
    • Florian Baumann, Irina Schulz, Bodo Rosenhahn
      Multi-Sensor Acceleration-based Action Recognition
      International Conference on Image Analysis and Recognition (ICIAR), October 2014
    • Florian Baumann, Li Fangda, Arne Ehlers, Rosenhahn Bodo
      Thresholding a Random Forest Classifier
      Advances in Visual Computing - 10th International Symposium, Springer, Las Vegas, NV, USA, December 2014, edited by George Bebis et al.
    • Oliver Jakob Arndt, Björn Scheuermann, Bodo Rosenhahn
      "Region Cut" - Interactive Multi-Label Segmentation Utilizing Cellular Automaton
      IEEE Workshop on Applications of Computer Vision (WACV), Clearwater Beach, Florida, USA, January 2013
    • Michael Ying Yang
      Image Segmentation by Bilayer Superpixel Grouping
      Asian Conference on Pattern Recognition , accpeted for publication, Okinawa, Japan, November 2013
    • Florian Baumann, Arne Ehlers, Karsten Vogt, Bodo Rosenhahn
      Cascaded Random Forest for Fast Object Detection
      18th Scandinavian Conference on Image Analysis (SCIA), Espoo, Finland, June 2013
    • Florian Baumann
      Action Recognition with HOG-OF Features
      35th German Conference on Pattern Recognition (YRF at GCPR), Saarbrücken, Germany, September 2013
    • Michele Fenzi, Ralf Dragon, Laura Leal-Taixé, Bodo Rosenhahn, Jörn Ostermann
      3D Object Recognition and Pose Estimation for Multiple Objects using Multi-Prioritized RANSAC and Model Updating
      Annual Symposium of the German Association for Pattern Recognition (DAGM), accepted as oral presentation , Graz, Austria, August 2012
    • Ralf Dragon, Bodo Rosenhahn, Jörn Ostermann
      Multi-Scale Clustering of Frame-to-Frame Correspondences for Motion Segmentation
      12th European Conference on Computer Vision (ECCV 2012), Florence, October 2012
    • Björn Scheuermann, Markus Schlosser, Bodo Rosenhahn
      Efficient Pixel-Grouping based on Dempster's Theory of Evidence for Image Segmentation
      The 11th Asian Conference on Computer Vision (ACCV), Lecture Notes in Computer Science (LNCS), Springer Berlin/Heidelberg, Vol. 7726, Daejeon, Korea, November 2012, edited by Kyoung Mu Lee, Jim Rehg, Yasuyuki Matsushita, and Zhanyi Hu
    • Michael Ying Yang, Wolfgang Förstner
      A Hierarchical Conditional Random Field Model for Labeling and Classifying Images of Man-made Scenes
      ICCV Workshop on Computer Vision for Remote Sensing of the Environment , IEEE, p. 196 – 203, 2011
    • M. Shoaib, T. Elbrandt, R. Dragon, J. Ostermann
      Altcare: Safe Living For Elderly People
      4th International ICST Conference on Pervasive Computing Technologies for Healthcare 2010, IEEE, March 2010
    • Ralf Dragon, Muhammad Shoaib, Bodo Rosenhahn, Jörn Ostermann
      NF-Features - No-Feature-Features for Representing non-Textured Regions
      11th European Conference on Computer Vision (ECCV 2010), Heraklion, Greece, September 2010
    • M. Shoaib, R. Dragon, J. Ostermann
      Shadow Detection for Moving Humans Using Gradient-Based Background Subtraction
      ICASSP International Conference on Acoustics, Speech and Signal Processing, Taipei, Taiwan, April 2009
    • M. Shoaib, R. Dragon, J. Ostermann
      Improving Object Detection by Contour-Based Shadow Removal
      Zweiter Workshop optische Technologien – HOT, Hannover, November 2008
  • Journals
    • Yeremia Gunawan Adhisantoso, Jan Voges, Christian Rohlfing, Viktor Tunev, Jens-Rainer Ohm, Jörn Ostermann
      GVC: efficient random access compression for gene sequence variations
      BMC Bioinformatics, Vol. 24, No. 1, p. 121, March 2023
    • Yuren Cong, Michael Yang, Bodo Rosenhahn
      RelTR: Relation Transformer for Scene Graph Generation
      IEEE transactions on pattern analysis and machine intelligence (TPAMI), 2023
    • Ilona Rosenboom, Tobias Scheithauer, Fabian C. Friedrich, Sophia Pörtner, Lisa Hollstein, Marie‑Madlen Pust, Konstantinos Sifakis, Tom Wehrbein, Bodo Rosenhahn, Lutz Wiehlmann, Patrick Chhatwal, Burkhard Tümmler, Colin F Davenport
      Wochenende - modular and flexible alignment-based shotgun metagenome analysis
      BMC Genomics, Springer Nature, November 2022
    • Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter
      Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning
      Journal of Machine Learning Research (JMLR), Vol. 23, No. 261, p. 1−61, October 2022
    • Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, Frank Hutter
      SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
      Journal of Machine Learning Research (JMLR) -- MLOSS, Vol. 23, No. 54, pp. 1-9, January 2022
    • Jack Parker-Holder, Raghu Rajan, Xingyou Song, André Biedenkapp, Yingjie Miao, Theresa Eimer, Baohe Zhang, Vu Nguyen, Roberto Calandra, Aleksandra Faust, Frank Hutter, Marius Lindauer
      Automated Reinforcement Learning (AutoRL): A Survey and Open Problems
      Journal of Artificial Intelligence Research (JAIR), 2022
    • Jan Voges, Mikel Hernaez, Marco Mattavelli, Jörn Ostermann
      An Introduction to MPEG-G: The First Open ISO/IEC Standard for the Compression and Exchange of Genomic Sequencing Data
      Proceedings of the IEEE, Vol. 109, No. 9, pp. 1607-1622, September 2021
    • Cheng Hao, Wentong Liao, Xuejiao Tang, Michael Ying Yang, Monika Sester, Bodo Rosenhahn
      AMENet: Attentive Maps Encoder Network for Trajectory Prediction
      ISPRS Journal of Photogrammetry and Remote Sensing, Elsevier, Vol. 172, pp. 253--266, 2021
    • Lucas Zimmer, Marius Lindauer, Frank Hutter
      Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL
      IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE, Vol. 43, No. 9, pp. 3079 - 3090, August 2021
    • Zhengying Liu, Adrien Pavao, Zhen Xu, Sergio Escalera, Fabio Ferreira, Isabelle Gyon, Sirui Hong, Frank Hutter, Rongrong Ji, Julio Jacques Junior, Ge Li, Marius Lindauer, Zhipeng Luo, Meysam Madadi, Thomas Nierhoff, Kangning Niu, Chunguang Pan, Danny Stoll, Sebastien Treguer, Wang Jin, Peng Wang, Chenglin Wu, Xiong Youcheng, Arber Zela, Yang Zhang
      Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019
      IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE, Vol. 43, No. 9, pp. 3108 - 3125, August 2021
    • Jan Voges, Tom Paridaens, Fabian Müntefering, Liudmila S. Mainzer, Brian Bliss, Mingyu Yang, Idoia Ochoa, Jan Fostier, Jörn Ostermann, Mikel Hernaez
      GABAC: an arithmetic coding solution for genomic data
      Bioinformatics, Vol. 36, No. 7, pp. 2275-2277, April 2020
    • Marius Lindauer and Frank Hutter
      Best Practices for Scientific Research on Neural Architecture Search
      Journal of Machine Learning Research, Vol. 21, pp. 1-18, December 2020
    • Jan Voges, Jörn Ostermann, Mikel Hernaez
      CALQ: compression of quality values of aligned sequencing data
      Bioinformatics, Vol. 34, No. 10, pp. 1650-1658, May 2018
    • Jan Voges, Ali Fotouhi, Jörn Ostermann, M. Oguzhan Külekci
      A Two-level Scheme for Quality Score Compression
      Journal of Computational Biology, Vol. 25, No. 10, pp. 1141-1151, October 2018
    • Michael Ying Yang, Wentong Liao, Hanno Ackermann, Bodo Rosenhahn
      On support relations and semantic scene graphs
      ISPRS Journal of Photogrammetry and Remote Sensing, Elsevier, Vol. 131, pp. 15-25, July 2017
    • Ibrahim Numanagic, James K. Bonfield, Faraz Hach, Jan Voges, Jörn Ostermann, Claudio Alberti, Marco Mattavelli, S. Cenk Sahinalp
      Comparison of high-throughput sequencing data compression tools
      Nature Methods, Vol. 13, No. 12, pp. 1005-1008, December 2016
    • W. Huang, X. Gong, Michael Ying Yang
      Joint object segmentation and depth upsampling
      Signal Processing Letters, IEEE, Vol. 22, No. 2, p. 192–196, 2015
    • Florian Baumann, Jie Liao, Arne Ehlers, Bodo Rosenhahn
      Recognizing Human Actions using novel Space-time Volume Binary Patterns
      Neurocomputing Journal (to appear), April 2015
    • Ralf Dragon, Carsten Dolar, Jörn Ostermann, Matthias Rieger, Holger Blume, Fabian Abel, Philipp Kärger
      Intelligente Videoüberwachung
      UniMagazin, Vol. 3, pp. 34-37, December 2010
  • Books
    • Jan Voges
      Compression of DNA Sequencing Data
      VDI Verlag, 2022
  • Book Chapters
    • Hector Mendoza and Aaron Klein and Matthias Feurer and Jost Tobias Springenberg and Matthias Urban and Michael Burkart and Max Dippel and Marius Lindauer and Frank Hutter
      Towards Automatically-Tuned Deep Neural Networks
      AutoML: Methods, Sytems, Challenges, Springer, pp. 141--156, December 2018, edited by Hutter, Frank and Kotthoff, Lars and Vanschoren, Joaquin
  • Technical Report
    • Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor Awad, Theresa Eimer, Marius Lindauer, Frank Hutter
      Automated Dynamic Algorithm Configuration
      ArXiv, May 2022
    • René Sass, Eddie Bergman, André Biedenkapp, Frank Hutter, Marius Lindauer
      DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning
      Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML@ICML’22), p. 6, June 2022
    • Katharina Eggensperger, Kai Haase, Philipp Müller, Marius Lindauer, Frank Hutter
      Neural Model-based Optimization with Right-Censored Observations
      CoRR, ArXiv, September 2020
    • M. Lindauer and K. Eggensperger and M. Feurer and A. Biedenkapp and J. Marben and P. M\"uller and F. Hutter
      BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters
      arXiv:1908.06756 [cs.LG], August 2019