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Manuel Schmitt
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2020 – today
- 2023
- [j6]Manuel Schmitt
, Ingo Krossing
Terminal end-on coordination of dinitrogen versus isoelectronic CO: A comparison using the charge displacement analysis. J. Comput. Chem. 44(3): 149-158 (2023) - 2022
- [j5]Moritz Mühlenthaler, Alexander Raß
Exact Markov chain-based runtime analysis of a discrete particle swarm optimization algorithm on sorting and OneMax. Nat. Comput. 21(4): 651-677 (2022) - [c13]Christian Bergler, Manuel Schmitt, Andreas K. Maier, Rachael Xi Cheng, Volker Barth, Elmar Nöth:
ORCA-PARTY: An Automatic Killer Whale Sound Type Separation Toolkit Using Deep Learning. ICASSP 2022: 1046-1050 - [c12]Christian Bergler, Alexander Barnhill
ORCA-WHISPER: An Automatic Killer Whale Sound Type Generation Toolkit Using Deep Learning. INTERSPEECH 2022: 2413-2417 - 2021
- [c11]Christian Bergler, Manuel Schmitt, Andreas K. Maier, Helena Symonds, Paul Spong, Steven R. Ness, George Tzanetakis, Elmar Nöth:
ORCA-SLANG: An Automatic Multi-Stage Semi-Supervised Deep Learning Framework for Large-Scale Killer Whale Call Type Identification. Interspeech 2021: 2396-2400 - 2020
- [j4]Bernd Bassimir
Self-adaptive potential-based stopping criteria for Particle Swarm Optimization with forced moves. Swarm Intell. 14(4): 285-311 (2020) - [c10]Christian Bergler, Manuel Schmitt, Andreas Maier, Simeon Smeele, Volker Barth, Elmar Nöth:
ORCA-CLEAN: A Deep Denoising Toolkit for Killer Whale Communication. INTERSPEECH 2020: 1136-1140
2010 – 2019
- 2019
- [j3]Bernd Bassimir
Theory of particle swarm optimization: A survey of the power of the swarm's potential. it Inf. Technol. 61(4): 169-176 (2019) - [c9]Christian Bergler, Manuel Schmitt, Rachael Xi Cheng, Andreas K. Maier, Volker Barth, Elmar Nöth:
Deep Learning for Orca Call Type Identification - A Fully Unsupervised Approach. INTERSPEECH 2019: 3357-3361 - [c8]Christian Bergler, Manuel Schmitt, Rachael Cheng, Hendrik Schröter, Andreas K. Maier, Volker Barth, Michael Weber, Elmar Nöth:
Deep Representation Learning for Orca Call Type Classification. TSD 2019: 274-286 - [i6]Moritz Mühlenthaler, Alexander Raß, Manuel Schmitt, Rolf Wanka:
Exact Markov Chain-based Runtime Analysis of a Discrete Particle Swarm Optimization Algorithm on Sorting and OneMax. CoRR abs/1902.01810 (2019) - [i5]Bernd Bassimir, Manuel Schmitt, Rolf Wanka:
Self-adaptive Potential-based Stopping Criteria for Particle Swarm Optimization. CoRR abs/1906.08867 (2019) - 2017
- [c7]Moritz Mühlenthaler, Alexander Raß
Runtime Analysis of a Discrete Particle Swarm Optimization Algorithm on Sorting and OneMax. FOGA 2017: 13-24 - 2015
- [j2]Manuel Schmitt, Rolf Wanka
Particle swarm optimization almost surely finds local optima. Theor. Comput. Sci. 561: 57-72 (2015) - [c6]Lydia Schwab, Manuel Schmitt, Rolf Wanka:
Multimodal medical image registration using particle swarm optimization with influence of the data's initial orientation. CIBCB 2015: 1-8 - [c5]Alexander Raß
Explanation of Stagnation at Points that are not Local Optima in Particle Swarm Optimization by Potential Analysis. GECCO (Companion) 2015: 1463-1464 - [p1]Manuel Schmitt:
Konvergenzanalyse für die Partikelschwarmoptimierung. Ausgezeichnete Informatikdissertationen 2015: 259-268 - [i4]Alexander Raß, Manuel Schmitt, Rolf Wanka:
Explanation of Stagnation at Points that are not Local Optima in Particle Swarm Optimization by Potential Analysis. CoRR abs/1504.08241 (2015) - 2014
- [c4]Vanessa Lange
Towards a Better Understanding of the Local Attractor in Particle Swarm Optimization: Speed and Solution Quality. ICAIS 2014: 90-99 - [c3]Bernd Bassimir
How Much Forcing Is Necessary to Let the Results of Particle Swarms Converge? ICSIBO 2014: 98-105 - [i3]Vanessa Lange, Manuel Schmitt, Rolf Wanka:
Towards a Better Understanding of the Local Attractor in Particle Swarm Optimization: Speed and Solution Quality. CoRR abs/1406.1691 (2014) - 2013
- [j1]Manuel Schmitt, Rolf Wanka
Exploiting independent subformulas: A faster approximation scheme for #k-SAT. Inf. Process. Lett. 113(9): 337-344 (2013) - [c2]Manuel Schmitt, Rolf Wanka
Particles prefer walking along the axes: experimental insights into the behavior of a particle swarm. GECCO (Companion) 2013: 17-18 - [c1]Manuel Schmitt, Rolf Wanka
Particle swarm optimization almost surely finds local optima. GECCO 2013: 1629-1636 - [i2]Manuel Schmitt, Rolf Wanka:
Exploiting Independent Subformulas: A Faster Approximation Scheme for #k-SAT. CoRR abs/1302.6900 (2013) - [i1]Manuel Schmitt, Rolf Wanka:
Particles Prefer Walking Along the Axes: Experimental Insights into the Behavior of a Particle Swarm. CoRR abs/1303.6145 (2013)
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