Sebastian Schmitt

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	Semua	Sejak 2020
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indeks-h	19	13
indeks-i10	28	17

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OlhoferHonda Research Institute EuropeEmail yang diverifikasi di honda-ri.de
Thomas BäckProfessor of Computer Science, Leiden University; Chief Scientist, NORCE Research Centre, NorwayEmail yang diverifikasi di liacs.leidenuniv.nl
Yaochu Jin, Chair Professor, MAE, Re...Department of Artificial Intelligence, Westlake University, ChinaEmail yang diverifikasi di westlake.edu.cn
Xilu WangUniversity of SurreyEmail yang diverifikasi di surrey.ac.uk
Andrea Castellani, PhDSenior Scientist at Honda Research Institute Europe GmbH (HRI-EU)Email yang diverifikasi di honda-ri.de
Frithjof B AndersTechnische University DortmundEmail yang diverifikasi di tu-dortmund.de
stefano squartiniFull Professor in Electrical Engineering, Università Politecnica delle MarcheEmail yang diverifikasi di univpm.it
Sheir YarkoniLeiden UniversityEmail yang diverifikasi di qhipu.ai
Elena RaponiLeiden University (LIACS)Email yang diverifikasi di liacs.leidenuniv.nl
Barbara HammerProfessor, Bielefeld UniversityEmail yang diverifikasi di techfak.uni-bielefeld.de
Philipp HaukeUniversity of TrentoEmail yang diverifikasi di mytum.de
Felix LanfermannHonda Research Institute EuropeEmail yang diverifikasi di honda-ri.de
Patricia WollstadtHonda Research Institute Europe GmbHEmail yang diverifikasi di honda-ri.de
Hao WangLeiden UniversityEmail yang diverifikasi di liacs.leidenuniv.nl
Steve LenkResearcher, Fraunhofer IOSB-ASTEmail yang diverifikasi di iosb-ast.fraunhofer.de
Maciej LewensteinICFO - Institute of Photonic SciencesEmail yang diverifikasi di icfo.eu
Valentin KasperKRC S.L.Email yang diverifikasi di icfo.net
Alberto BottarelliPhd Student, University of TrentoEmail yang diverifikasi di unitn.it
Manuel S. RudolphPhD Candidate at EPFL - Working on quantum computing algorithms and classical simulation methodsEmail yang diverifikasi di epfl.ch
Linus EkstrømPhD StudentEmail yang diverifikasi di honda-ri.de

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Sebastian Schmitt

Honda Research Institute Europe GmbH

Email yang diverifikasi di honda-ri.de

Real-world machine learning applications anomaly detection optimization quantum physics quantum computing


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
Quantum Annealing for Industry Applications: Introduction and Review S Yarkoni, E Raponi, T Bäck, S Schmitt Reports on Progress in Physics 85 (10), 104001, 2022	311	2022
Recent Advances in Bayesian Optimization X Wang, Y Jin, S Schmitt, M Olhofer ACM Computing Surveys, 2023	285	2023
Real photon scattering up to 10 MeV: the improved facility at the Darmstadt electron accelerator S-DALINAC P Mohr, J Enders, T Hartmann, H Kaiser, D Schiesser, S Schmitt, S Volz, ... Nuclear Instruments and Methods in Physics Research Section A: Accelerators …, 1999	152	1999
Real-World Anomaly Detection by Using Digital Twin Systems and Weakly Supervised Learning A Castellani, S Schmitt, S Squartini IEEE Transactions on Industrial Informatics 17 (7), 4733-4742, 2020	151	2020
An adaptive Bayesian approach to surrogate-assisted evolutionary multi-objective optimization X Wang, Y Jin, S Schmitt, M Olhofer Information Sciences 519, 317-331, 2020	133	2020
Comparison between scattering-states numerical renormalization group and the Kadanoff-Baym-Keldysh approach to quantum transport: Crossover from weak to strong correlations S Schmitt, FB Anders Physical Review B—Condensed Matter and Materials Physics 81 (16), 165106, 2010	42	2010
A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition P Wollstadt, S Schmitt, M Wibral Journal of Machine Learning Research 24 (131), 1--44, 2023	40	2023
study of Ru-based perovskite and E Jakobi, S Kanungo, S Sarkar, S Schmitt, T Saha-Dasgupta Physical Review B—Condensed Matter and Materials Physics 83 (4), 041103, 2011	40	2011
Transfer learning based surrogate assisted evolutionary bi-objective optimization for objectives with different evaluation times X Wang, Y Jin, S Schmitt, M Olhofer, R Allmendinger Knowledge-Based Systems 227, 107190, 2021	35	2021
Non-Fermi-liquid signatures in the Hubbard model due to van Hove singularities S Schmitt Physical Review B—Condensed Matter and Materials Physics 82 (15), 155126, 2010	35	2010
Conserving approximations in direct perturbation theory: new semianalytical impurity solvers and their application to general lattice problems N Grewe, S Schmitt, T Jabben, FB Anders Journal of Physics: Condensed Matter 20 (36), 365217, 2008	35	2008
Nonequilibrium Zeeman splitting in quantum transport through nanoscale junctions S Schmitt, FB Anders Physical review letters 107 (5), 056801, 2011	31	2011
Quantum approximate optimization algorithm for qudit systems Y Deller, S Schmitt, M Lewenstein, S Lenk, F Federer , M, Jendrzejewski, ... Phys. Rev. A 107, 062410, 2023	29	2023
Transfer learning for gaussian process assisted evolutionary bi-objective optimization for objectives with different evaluation times X Wang, Y Jin, S Schmitt, M Olhofer Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 587-594, 2020	29	2020
Spectral properties of the two-impurity Anderson model with varying distance and various interactions T Jabben, N Grewe, S Schmitt Physical Review B—Condensed Matter and Materials Physics 85 (4), 045133, 2012	26	2012
Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noise A Castellani, S Schmitt, B Hammer Machine Learning and Knowledge Discovery in Databases. Research Track. (ECML …, 2021	25	2021
Task-Sensitive Concept Drift Detector with Constraint Embedding A Castellani, S Schmitt, B Hammer 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 01-08, 2021	23	2021
Transfer Learning Based Co-Surrogate Assisted Evolutionary Bi-Objective Optimization for Objectives with Non-Uniform Evaluation Times X Wang, Y Jin, S Schmitt, M Olhofer Evolutionary computation 30 (2), 221-251, 2022	22	2022
Kinks in the electronic dispersion of the Hubbard model away from half filling P Grete, S Schmitt, C Raas, FB Anders, GS Uhrig Physical Review B—Condensed Matter and Materials Physics 84 (20), 205104, 2011	21	2011
Anomaly Detection in Univariate Time Series: An Empirical Comparison of Machine Learning Algorithms. S Däubener, S Schmitt, H Wang, P Krause, T Bäck ICDM, 161-175, 2019	19	2019

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