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TU Berlin

Inhalt des Dokuments

Prof. Dr. Manfred Opper

Lupe
  • Leiter der Einheit KI
  • Raum: MAR 4.017
  • Telefon: +4930 314-73749
  • E-Mail: manfred.opper <AT> tu-berlin.de

 

 


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Publikationen

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Dynamical Functional Theory for Compressed Sensing

Burak Çakmak and Manfred Opper and Ole Winther and Bernard H. Fleury

2017 IEEE International Symposium on Information Theory (ISIT). IEEE Press, 2143-2147. 2017

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DARA: Estimating the Behavior of Data Rate Adaptation Algorithms in WLAN Hotspots

Sven Wiethölter and Andreas Ruttor and Uwe Bergemann and Manfred Opper and Adam Wolisz

2013

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DARA: Estimating the Behavior of Data Rate Adaptation Algorithms in WLAN Hotspots

Sven Wiethölder and Andreas Ruttor and Uwe Bergemann and Manfred Opper and Adam Wolisz

2013

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The effect of correlations in neural networks

Andreas Wendemuth and Manfred Opper and Wolfgang Kinzel

J. Phys. A, 3165–3185. 1993

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V

Variational mean-field algorithm for efficient inference in large systems of stochastic differential equations

Michail D. Vrettas and Dan Cornford and Manfred Opper

Physical Review E, 012148. 2015

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Estimating parameters in stochastic systems: A variational Bayesian approach

Michail D. Vrettas and Dan Cornford and Manfred Opper

Physica D: Nonlinear Phenomena. Elsevier, 1877-1900. 2011

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T

Finite Dimensional Approximation of Gaussian Processes

Giancarlo Ferrari Trecate and Christopher K.I. Williams and Manfred Opper

Advances in Neural Information Processing Systems 11. MIT Press, 218-224. 1999

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S

Dynamic state estimation based on Poisson spike trains—towards a theory of optimal encoding

Alex Susemihl and Ron Meir and Manfred Opper

Journal of Statistical Mechanics: Theory and Experiment, P03009. 2013

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Analytical Results for the Error in Filtering of Gaussian Processes

Alex Susemihl and Manfred Opper and Ron Meir

Advances in Neural Information Processing Systems 24. Curran Associates, Inc., 2303–2311. 2011

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MCMC for continuous time switching models

Florian Stimberg and Andreas Ruttor and Manfred Opper

NIPS Workshop on Monte Carlo Methods for Modern Applications (December 10, 2010, Whistler, Canada) 2010

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Bayesian Inference for Models of Transcriptional Regulation Using Markov Chain Monte Carlo Sampling

Florian Stimberg and Andreas Ruttor and Manfred Opper

Proceedings of the 8th International Workshop on Computational Systems Biology (WCSB). Tampere University of Technology, Tampere, Finland, 169–172. 2011

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Inference in continuous-time change-point models

Florian Stimberg and Manfred Opper and Guido Sanguinetti and Andreas Ruttor

Advances in Neural Information Processing Systems 24, 2717–2725. 2011

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Bayesian Inference for Change Points in Dynamical Systems with Reusable States—a Chinese Restaurant Process Approach

Florian Stimberg and Manfred Opper and Andreas Ruttor

Proceedings of the International Conference on Artificial Intelligence and Statistics 2012

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Variational Markov Chain Monte Carlo for Inference in Partially Observed Nonlinear Diffusions

Yuan Shen and Cédric Archambeau and Dan Cornford and Manfred Opper

Proceedings of the Workshop Inference and Estimation in Probabilistic Time-Series Models, 67-78. 2008

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A comparison of variational and Markov Chain Monte Carlo methods for inference in partially observed stochastic dynamic systems

Yuan Shen and Cédric Archambeau and Dan Cornford and Manfred Opper and John Shawe-Taylor and Remi Barillec

Journal of Signal Processing Systems 2009

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Postadresse

TU Berlin
Fakultät IV
Elektrotechnik und Informatik
Sekr. MAR 4-2
Marchsstrasse 23
D-10587 Berlin