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ORCIDThomas A. Runkler
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- affiliation: Technical University Munich, Germany
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2020 – today
- 2024
[j40]Thomas A. Runkler:
Einstein consistency of fuzzy preference relations. J. Intell. Fuzzy Syst. 46(3): 6565-6576 (2024)- [j39]Dickson Odhiambo Owuor
, Thomas A. Runkler, Anne Laurent, Lesley Bonyo:
Clustering-based gradual pattern mining. Int. J. Mach. Learn. Cybern. 15(6): 2263-2281 (2024) - [j38]Haoyu Ren, Darko Anicic, Xue Li, Thomas A. Runkler:
On-device Online Learning and Semantic Management of TinyML Systems. ACM Trans. Embed. Comput. Syst. 23(4): 55:1-55:32 (2024) - [i42]Aneta Koleva, Martin Ringsquandl, Ahmed Hatem, Thomas A. Runkler, Volker Tresp:
Wiki-TabNER: Advancing Table Interpretation Through Named Entity Recognition. CoRR abs/2403.04577 (2024) - [i41]Simon Eisenmann, Daniel Hein, Steffen Udluft, Thomas A. Runkler:
Model-based Offline Quantum Reinforcement Learning. CoRR abs/2404.10017 (2024) - [i40]Cuong Nhat Ha, Shima Asaadi, Sanjeev Kumar Karn, Oladimeji Farri, Tobias Heimann, Thomas A. Runkler:
Fusion of Domain-Adapted Vision and Language Models for Medical Visual Question Answering. CoRR abs/2404.16192 (2024) - [i39]Haoyu Ren, Xue Li, Darko Anicic, Thomas A. Runkler:
On-device Online Learning and Semantic Management of TinyML Systems. CoRR abs/2405.07601 (2024) - [i38]Yongjian Tang, Rakebul Hasan, Thomas A. Runkler:
FsPONER: Few-shot Prompt Optimization for Named Entity Recognition in Domain-specific Scenarios. CoRR abs/2407.08035 (2024) - 2023
- [c104]Phillip Swazinna, Steffen Udluft, Thomas A. Runkler:
Automatic Trade-off Adaptation in Offline RL. ESANN 2023 - [c103]Thomas A. Runkler:
A Convergence Study of the Possibilistic One Means Algorithm. FUZZ 2023: 1-6 - [c102]Phillip Swazinna, Steffen Udluft, Thomas A. Runkler:
User-Interactive Offline Reinforcement Learning. ICLR 2023 - [c101]Haoyu Ren, Darko Anicic, Thomas A. Runkler:
TinyReptile: TinyML with Federated Meta-Learning. IJCNN 2023: 1-9 - [p4]Anna Himmelhuber, Stephan Grimm, Mitchell Joblin, Sonja Zillner, Thomas A. Runkler:
Combining Sub-Symbolic and Symbolic Methods for Explainability. Compendium of Neurosymbolic Artificial Intelligence 2023: 559-576 - [i37]Haoyu Ren, Darko Anicic, Thomas A. Runkler:
TinyReptile: TinyML with Federated Meta-Learning. CoRR abs/2304.05201 (2023) - [i36]Phillip Swazinna, Steffen Udluft, Thomas A. Runkler:
Automatic Trade-off Adaptation in Offline RL. CoRR abs/2306.09744 (2023) - [i35]Haoyu Ren, Xue Li, Darko Anicic, Thomas A. Runkler:
TinyMetaFed: Efficient Federated Meta-Learning for TinyML. CoRR abs/2307.06822 (2023) - 2022
- [j37]Thomas A. Runkler:
PIU: risk-sensitive decision making using Pareto optimization of interval utilities induced by fuzzy preference relations. Soft Comput. 26(1): 1-11 (2022) - [j36]Dickson Odhiambo Owuor, Thomas A. Runkler, Anne Laurent:
A metaheuristic approach for mining gradual patterns. Swarm Evol. Comput. 75: 101205 (2022) - [j35]Haoyu Ren, Darko Anicic, Thomas A. Runkler:
Towards Semantic Management of On-Device Applications in Industrial IoT. ACM Trans. Internet Techn. 22(4): 102:1-102:30 (2022) - [c100]Ahmed Frikha, Haokun Chen, Denis Krompaß, Thomas A. Runkler, Volker Tresp:
Towards Data-Free Domain Generalization. ACML 2022: 327-342 - [c99]Thomas A. Runkler:
Pareto Interval Type-2 Fuzzy Decision Making for Labeled Objects. FUZZ-IEEE 2022: 1-6 - [c98]Dominik Dold, Josep Soler Garrido, Victor Caceres Chian, Marcel Hildebrandt, Thomas A. Runkler:
Neuro-symbolic computing with spiking neural networks. ICONS 2022: 30:1-30:4 - [c97]Haoyu Ren, Kirill Dorofeev, Darko Anicic, Youssef Hammad, Roland Eckl, Thomas A. Runkler:
SeLoC-ML: Semantic Low-Code Engineering for Machine Learning Applications in Industrial IoT. ISWC 2022: 845-862 - [c96]Anna Himmelhuber, Dominik Dold, Stephan Grimm, Sonja Zillner, Thomas A. Runkler:
Detection, Explanation and Filtering of Cyber Attacks Combining Symbolic and Sub-Symbolic Methods. SSCI 2022: 381-388 - [i34]Phillip Swazinna, Steffen Udluft, Daniel Hein, Thomas A. Runkler:
Comparing Model-free and Model-based Algorithms for Offline Reinforcement Learning. CoRR abs/2201.05433 (2022) - [i33]Haoyu Ren, Darko Anicic, Thomas A. Runkler:
How to Manage Tiny Machine Learning at Scale: An Industrial Perspective. CoRR abs/2202.09113 (2022) - [i32]Phillip Swazinna, Steffen Udluft, Thomas A. Runkler:
User-Interactive Offline Reinforcement Learning. CoRR abs/2205.10629 (2022) - [i31]Haoyu Ren, Kirill Dorofeev, Darko Anicic, Youssef Hammad, Roland Eckl, Thomas A. Runkler:
SeLoC-ML: Semantic Low-Code Engineering for Machine Learning Applications in Industrial IoT. CoRR abs/2207.08818 (2022) - [i30]Dominik Dold, Josep Soler Garrido, Victor Caceres Chian, Marcel Hildebrandt, Thomas A. Runkler:
Neuro-symbolic computing with spiking neural networks. CoRR abs/2208.02576 (2022) - [i29]Dickson Odhiambo Owuor, Thomas A. Runkler, Anne Laurent, Joseph Orero, Edmond Odhiambo Menya:
Ant Colony Optimization for Mining Gradual Patterns. CoRR abs/2208.14795 (2022) - [i28]Dickson Odhiambo Owuor, Thomas A. Runkler, Anne Laurent:
A Metaheuristic Approach for Mining Gradual Patterns. CoRR abs/2211.07940 (2022) - [i27]Anna Himmelhuber, Dominik Dold, Stephan Grimm, Sonja Zillner, Thomas A. Runkler:
Detection, Explanation and Filtering of Cyber Attacks Combining Symbolic and Sub-Symbolic Methods. CoRR abs/2212.13991 (2022) - 2021
- [j34]Phillip Swazinna, Steffen Udluft, Thomas A. Runkler:
Overcoming model bias for robust offline deep reinforcement learning. Eng. Appl. Artif. Intell. 104: 104366 (2021) - [j33]Dickson Odhiambo Owuor, Thomas A. Runkler, Anne Laurent, Joseph Onderi Orero, Edmond Odhiambo Menya:
Ant colony optimization for mining gradual patterns. Int. J. Mach. Learn. Cybern. 12(10): 2989-3009 (2021) - [c95]Haoyu Ren, Darko Anicic, Thomas A. Runkler:
The synergy of complex event processing and tiny machine learning in industrial IoT. DEBS 2021: 126-135 - [c94]Hiba Arnout, Johanna Bronner, Thomas A. Runkler:
Differentially Private Time Series Generation. ESANN 2021 - [c93]Phillip Swazinna, Steffen Udluft, Daniel Hein, Thomas A. Runkler:
Behavior Constraining in Weight Space for Offline Reinforcement Learning. ESANN 2021 - [c92]Hiba Arnout, Johanna Bronner, Thomas A. Runkler:
Evaluation of Generative Adversarial Networks for Time Series Data. IJCNN 2021: 1-7 - [c91]Haoyu Ren, Darko Anicic, Thomas A. Runkler:
TinyOL: TinyML with Online-Learning on Microcontrollers. IJCNN 2021: 1-8 - [c90]Anna Himmelhuber, Sonja Zillner, Stephan Grimm, Martin Ringsquandl, Mitchell Joblin, Thomas A. Runkler:
A New Concept for Explaining Graph Neural Networks. NeSy 2021: 1-5 - [c89]Anna Himmelhuber, Mitchell Joblin, Martin Ringsquandl, Thomas A. Runkler:
Demystifying Graph Neural Network Explanations. PKDD/ECML Workshops (1) 2021: 67-75 - [c88]Anna Himmelhuber, Stephan Grimm, Sonja Zillner, Mitchell Joblin, Martin Ringsquandl, Thomas A. Runkler:
Combining Sub-symbolic and Symbolic Methods for Explainability. RuleML+RR 2021: 172-187 - [c87]Hiba Arnout, Johanna Bronner, Johannes Kehrer, Thomas A. Runkler:
Translation of Time Series Data from Controlled DC Motors using Disentangled Representation Learning. SSCI 2021: 1-8 - [c86]Hiba Arnout, Johanna Bronner, Thomas A. Runkler:
ClaRe-GAN: Generation of Class-Specific Time Series. SSCI 2021: 1-8 - [c85]Phillip Swazinna, Steffen Udluft, Thomas A. Runkler:
Measuring Data Quality for Dataset Selection in Offline Reinforcement Learning. SSCI 2021: 1-8 - [i26]Haoyu Ren, Darko Anicic, Thomas A. Runkler:
TinyOL: TinyML with Online-Learning on Microcontrollers. CoRR abs/2103.08295 (2021) - [i25]Haoyu Ren, Darko Anicic, Thomas A. Runkler:
The Synergy of Complex Event Processing and Tiny Machine Learning in Industrial IoT. CoRR abs/2105.03371 (2021) - [i24]Phillip Swazinna, Steffen Udluft, Daniel Hein, Thomas A. Runkler:
Behavior Constraining in Weight Space for Offline Reinforcement Learning. CoRR abs/2107.05479 (2021) - [i23]Victor Caceres Chian, Marcel Hildebrandt, Thomas A. Runkler, Dominik Dold:
Learning through structure: towards deep neuromorphic knowledge graph embeddings. CoRR abs/2109.10376 (2021) - [i22]Ahmed Frikha, Haokun Chen, Denis Krompaß, Thomas A. Runkler, Volker Tresp:
Towards Data-Free Domain Generalization. CoRR abs/2110.04545 (2021) - [i21]Anna Himmelhuber, Mitchell Joblin, Martin Ringsquandl, Thomas A. Runkler:
Demystifying Graph Neural Network Explanations. CoRR abs/2111.12984 (2021) - [i20]Anna Himmelhuber, Stephan Grimm, Thomas A. Runkler, Sonja Zillner:
Ontology-Based Skill Description Learning for Flexible Production Systems. CoRR abs/2111.13142 (2021) - [i19]Phillip Swazinna, Steffen Udluft, Thomas A. Runkler:
Measuring Data Quality for Dataset Selection in Offline Reinforcement Learning. CoRR abs/2111.13461 (2021) - [i18]Anna Himmelhuber, Stephan Grimm, Sonja Zillner, Mitchell Joblin, Martin Ringsquandl, Thomas A. Runkler:
Combining Sub-Symbolic and Symbolic Methods for Explainability. CoRR abs/2112.01844 (2021) - 2020
- [j32]Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek:
Bayesian decomposition of multi-modal dynamical systems for reinforcement learning. Neurocomputing 416: 352-359 (2020) - [j31]Thomas A. Runkler:
PrefMap: Visualization of fuzzy pairwise preference structures. J. Intell. Fuzzy Syst. 39(3): 4027-4040 (2020) - [c84]Yatin Chaudhary, Pankaj Gupta, Khushbu Saxena, Vivek Kulkarni, Thomas A. Runkler, Hinrich Schütze:
TopicBERT for Energy Efficient Document Classification. EMNLP (Findings) 2020: 1682-1690 - [c83]Anna Himmelhuber, Stephan Grimm, Thomas A. Runkler, Sonja Zillner:
Ontology-Based Skill Description Learning for Flexible Production Systems. ETFA 2020: 975-981 - [c82]Thomas A. Runkler, Chao Chen, Simon Coupland, Robert I. John:
Comparing Intervals Using Type Reduction. FUZZ-IEEE 2020: 1-6 - [c81]Pankaj Gupta, Yatin Chaudhary, Thomas A. Runkler, Hinrich Schütze:
Neural Topic Modeling with Continual Lifelong Learning. ICML 2020: 3907-3917 - [c80]Ivan Gocev, Stephan Grimm, Thomas A. Runkler:
Supporting Skill-based Flexible Manufacturing with Symbolic AI Methods. IECON 2020: 769-774 - [c79]Hiba Arnout, Johanna Bronner, Johannes Kehrer, Thomas A. Runkler:
DR-TiST: Disentangled Representation for Time Series Translation Across Application Domains. IJCNN 2020: 1-8 - [c78]Thomas A. Runkler:
Generalized Weak Transitivity of Preference. IPMU (1) 2020: 119-128 - [i17]Hiba Arnout, Johannes Kehrer, Johanna Bronner, Thomas A. Runkler:
Visual Evaluation of Generative Adversarial Networks for Time Series Data. CoRR abs/2001.00062 (2020) - [i16]Manuel A. Roehrl, Thomas A. Runkler, Veronika Brandtstetter, Michel Tokic, Stefan Obermayer:
Modeling System Dynamics with Physics-Informed Neural Networks Based on Lagrangian Mechanics. CoRR abs/2005.14617 (2020) - [i15]Pankaj Gupta, Yatin Chaudhary, Thomas A. Runkler, Hinrich Schütze:
Neural Topic Modeling with Continual Lifelong Learning. CoRR abs/2006.10909 (2020) - [i14]Daniel Hein, Steffen Limmer, Thomas A. Runkler:
Interpretable Control by Reinforcement Learning. CoRR abs/2007.09964 (2020) - [i13]Phillip Swazinna, Steffen Udluft, Thomas A. Runkler:
Overcoming Model Bias for Robust Offline Deep Reinforcement Learning. CoRR abs/2008.05533 (2020) - [i12]Yatin Chaudhary, Pankaj Gupta, Khushbu Saxena, Vivek Kulkarni, Thomas A. Runkler, Hinrich Schütze:
TopicBERT for Energy Efficient Document Classification. CoRR abs/2010.16407 (2020)
2010 – 2019
- 2019
- [c77]Pankaj Gupta, Subburam Rajaram, Hinrich Schütze, Thomas A. Runkler:
Neural Relation Extraction within and across Sentence Boundaries. AAAI 2019: 6513-6520 - [c76]Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek:
interpretable dynamics models for data-efficient reinforcement learning. ESANN 2019 - [c75]Thomas A. Runkler, James C. Bezdek:
Optimizing the C Index Using a Canonical Genetic Algorithm. EvoApplications 2019: 287-298 - [c74]Thomas A. Runkler, Chao Chen, Simon Coupland, Robert I. John:
Just-In-Time Supply Chain Management Using Interval Type-2 Fuzzy Decision Making. FUZZ-IEEE 2019: 1-6 - [c73]Daniel Hein, Steffen Udluft, Thomas A. Runkler:
Generating interpretable reinforcement learning policies using genetic programming. GECCO (Companion) 2019: 23-24 - [c72]Thomas A. Runkler:
Canonical Fuzzy Preference Relations. IFSA/NAFIPS 2019: 542-555 - [c71]Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek:
Data Association with Gaussian Processes. ECML/PKDD (2) 2019: 548-564 - [i11]Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek:
Interpretable Dynamics Models for Data-Efficient Reinforcement Learning. CoRR abs/1907.04902 (2019) - [i10]Pankaj Gupta, Khushbu Saxena, Usama Yaseen, Thomas A. Runkler, Hinrich Schütze:
Neural Architectures for Fine-Grained Propaganda Detection in News. CoRR abs/1909.06162 (2019) - [i9]Yatin Chaudhary, Pankaj Gupta, Thomas A. Runkler:
Lifelong Neural Topic Learning in Contextualized Autoregressive Topic Models of Language via Informative Transfers. CoRR abs/1909.13315 (2019) - 2018
- [j30]Daniel Hein, Steffen Udluft, Thomas A. Runkler:
Interpretable policies for reinforcement learning by genetic programming. Eng. Appl. Artif. Intell. 76: 158-169 (2018) - [j29]Thomas A. Runkler, Chao Chen, Robert I. John:
Type reduction operators for interval type-2 defuzzification. Inf. Sci. 467: 464-476 (2018) - [c70]Stefan Depeweg, José Miguel Hernández-Lobato, Steffen Udluft, Thomas A. Runkler:
Sensitivity analysis for predictive uncertainty. ESANN 2018 - [c69]Wenlong Wu, James M. Keller, Thomas A. Runkler:
Sequential Possibilistic One-Means Clustering with Dynamic Eta. FUZZ-IEEE 2018: 1-8 - [c68]Daniel Hein, Steffen Udluft, Thomas A. Runkler:
Generating interpretable fuzzy controllers using particle swarm optimization and genetic programming. GECCO (Companion) 2018: 1268-1275 - [c67]Evgeny Kharlamov, Ognjen Savkovic, Martin Ringsquandl, Guohui Xiao, Gulnar Mehdi, Elem Güzel Kalayci, Werner Nutt, Mikhail Roshchin, Ian Horrocks, Thomas A. Runkler:
Diagnostics of Trains with Semantic Diagnostics Rules. ILP 2018: 54-71 - [c66]Thomas A. Runkler:
Mapping Utilities to Transitive Preferences. IPMU (1) 2018: 127-139 - [c65]Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek:
Bayesian Alignments of Warped Multi-Output Gaussian Processes. NeurIPS 2018: 6995-7004 - [c64]Ivan Gocev, Stephan Grimm, Thomas A. Runkler:
Explanation of Action Plans Through Ontologies. OTM Conferences (2) 2018: 386-403 - [c63]Marcel Hildebrandt, Swathi Shyam Sunder, Serghei Mogoreanu, Ingo Thon, Volker Tresp, Thomas A. Runkler:
Configuration of Industrial Automation Solutions Using Multi-relational Recommender Systems. ECML/PKDD (3) 2018: 271-287 - [i8]Daniel Hein, Steffen Udluft, Thomas A. Runkler:
Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming. CoRR abs/1804.10960 (2018) - [i7]Pankaj Gupta, Subburam Rajaram, Hinrich Schütze, Bernt Andrassy, Thomas A. Runkler:
Neural Relation Extraction Within and Across Sentence Boundaries. CoRR abs/1810.05102 (2018) - [i6]Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek:
Multimodal Deep Gaussian Processes. CoRR abs/1810.07158 (2018) - 2017
- [j28]Daniel Hein, Alexander Hentschel, Thomas A. Runkler, Steffen Udluft:
Particle swarm optimization for generating interpretable fuzzy reinforcement learning policies. Eng. Appl. Artif. Intell. 65: 87-98 (2017) - [j27]Thomas A. Runkler, Simon Coupland, Robert I. John:
Interval type-2 fuzzy decision making. Int. J. Approx. Reason. 80: 217-224 (2017) - [c62]Gulnar Mehdi, Evgeny Kharlamov, Ognjen Savkovic, Guohui Xiao, Elem Güzel Kalayci, Sebastian Brandt, Ian Horrocks, Mikhail Roshchin, Thomas A. Runkler:
SemDia: Semantic Rule-Based Equipment Diagnostics Tool. CIKM 2017: 2507-2510 - [c61]Thomas A. Runkler, James M. Keller:
Sequential possibilistic one-means clustering. FUZZ-IEEE 2017: 1-6 - [c60]Daniel Hein, Steffen Udluft, Michel Tokic, Alexander Hentschel, Thomas A. Runkler, Volkmar Sterzing:
Batch reinforcement learning on the industrial benchmark: First experiences. IJCNN 2017: 4214-4221 - [c59]Gulnar Mehdi, Thomas A. Runkler, Mikhail Roshchin, Sindhu Suresh, Nguyen Quang:
Ontology-based integration of performance related data and models: An application to industrial turbine analytics. INDIN 2017: 251-256 - [c58]Gulnar Mehdi, Evgeny Kharlamov, Ognjen Savkovic, Guohui Xiao, Elem Güzel Kalayci, Sebastian Brandt, Ian Horrocks, Mikhail Roshchin, Thomas A. Runkler:
Semantic Rule-Based Equipment Diagnostics. ISWC (2) 2017: 314-333 - [c57]Gulnar Mehdi, Evgeny Kharlamov, Ognjen Savkovic, Guohui Xiao, Elem Guzel Kalayci, Sebastian Brandt, Ian Horrocks, Mikhail Roshchin, Thomas A. Runkler:
Semantic Rule-Based Equipment Diagnostic. ISWC (Posters, Demos & Industry Tracks) 2017 - [c56]Daniel Hein, Stefan Depeweg, Michel Tokic, Steffen Udluft, Alexander Hentschel, Thomas A. Runkler, Volkmar Sterzing:
A benchmark environment motivated by industrial control problems. SSCI 2017: 1-8 - [i5]Daniel Hein, Steffen Udluft, Michel Tokic, Alexander Hentschel, Thomas A. Runkler, Volkmar Sterzing:
Batch Reinforcement Learning on the Industrial Benchmark: First Experiences. CoRR abs/1705.07262 (2017) - [i4]Daniel Hein, Stefan Depeweg, Michel Tokic, Steffen Udluft, Alexander Hentschel, Thomas A. Runkler, Volkmar Sterzing:
A Benchmark Environment Motivated by Industrial Control Problems. CoRR abs/1709.09480 (2017) - [i3]Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek:
Bayesian Alignments of Warped Multi-Output Gaussian Processes. CoRR abs/1710.02766 (2017) - [i2]Daniel Hein, Steffen Udluft, Thomas A. Runkler:
Interpretable Policies for Reinforcement Learning by Genetic Programming. CoRR abs/1712.04170 (2017) - 2016
- [b3]Thomas A. Runkler:
Data Analytics - Models and Algorithms for Intelligent Data Analysis, Second Edition. Springer 2016, ISBN 978-3-658-14074-8, pp. 1-150 - [j26]Daniel Hein, Alexander Hentschel, Thomas A. Runkler, Steffen Udluft:
Reinforcement Learning with Particle Swarm Optimization Policy (PSO-P) in Continuous State and Action Spaces. Int. J. Swarm Intell. Res. 7(3): 23-42 (2016) - [j25]James C. Bezdek, Masud Moshtaghi, Thomas A. Runkler, Christopher Leckie:
The Generalized C Index for Internal Fuzzy Cluster Validity. IEEE Trans. Fuzzy Syst. 24(6): 1500-1512 (2016) - [c55]Thomas A. Runkler:
Generation of linguistic membership functions from word vectors. FUZZ-IEEE 2016: 993-999 - [c54]Gulnar Mehdi, Sebastian Brandt, Mikhail Roshchin, Thomas A. Runkler:
Towards Semantic Reasoning in Knowledge Management Systems. AI4KM@IJCAI 2016: 132-146 - [c53]Gulnar Mehdi, Sebastian Brandt, Mikhail Roshchin, Thomas A. Runkler:
Semantic Framework for Industrial Analytics and Diagnostics. IJCAI 2016: 4016-4017 - [c52]Thomas A. Runkler:
Constructing Preference Relations from Utilities and Vice Versa. IPMU (2) 2016: 547-558 - [c51]Thomas Setzer, Stefan Nickel, Christof Weinhardt, Ralph Grothmann, Thomas A. Runkler, Ralf Gitzel, Hansjörg Fromm:
Vorwort. MKWI 2016: 1191 - [i1]Daniel Hein, Alexander Hentschel, Thomas A. Runkler, Steffen Udluft:
Particle Swarm Optimization for Generating Fuzzy Reinforcement Learning Policies. CoRR abs/1610.05984 (2016) - 2015
- [j24]Sigurd Spieckermann, Siegmund Düll, Steffen Udluft, Alexander Hentschel, Thomas A. Runkler:
Exploiting similarity in system identification tasks with recurrent neural networks. Neurocomputing 169: 343-349 (2015) - [c50]Johannes Kroß, Andreas Brunnert, Christian Prehofer, Thomas A. Runkler, Helmut Krcmar:
Stream Processing on Demand for Lambda Architectures. EPEW 2015: 243-257 - [c49]Thomas A. Runkler, Vikram Ravindra:
Fuzzy Graph Clustering based on Non-Euclidean Relational Fuzzy c-Means. IFSA-EUSFLAT 2015 - [c48]Thomas A. Runkler, Simon Coupland, Robert Ivor John:
Properties of interval type-2 defuzzification operators. FUZZ-IEEE 2015: 1-7 - [c47]