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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

 

 


Sprechstunde nach Vereinbarung

Publikationen

2015

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

Download Bibtex Eintrag

Learning of couplings for random asymmetric kinetic Ising models revisited: random correlation matrices and learning curves

Ludovica Bachschmid-Romano and Manfred Opper

Journal of Statistical Mechanics: Theory and Experiment. IOP Publishing, P09016. 2015

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2013


Approximate Gaussian process inference for the drift of stochastic differential equations

Andreas Ruttor and Philipp Batz and Manfred Opper

Advances in Neural Information Processing Systems, 2040-2048. 2013

Link zur Publikation (PDF, 317,9 KB) Download Bibtex Eintrag

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

Link zur Publikation (PDF, 1,2 MB) Download Bibtex Eintrag

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

Link zur Publikation (PDF, 1,3 MB) Download Bibtex Eintrag

Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models

Manfred Opper and Ulrich Paquet and Ole Winther

Journal of Machine Learning Research, 2857-2898. 2013

Link zur Publikation (PDF, 551,6 KB) Download Bibtex Eintrag

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

Link zur Publikation Download Bibtex Eintrag

2012

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

Link zur Publikation (PDF, 379,7 KB) Download Bibtex Eintrag

Optimal control as a graphical model inference problem

Hilbert J. Kappen and Vincenc Gomez and Manfred Opper

Machine Learning. Springer, 159-182. 2012

Link zur Publikation (PDF, 858,7 KB) Download Bibtex Eintrag

2011

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

Link zur Publikation (PDF, 122,3 KB) Download Bibtex Eintrag

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

Link zur Publikation (PDF, 393,8 KB) Download Bibtex Eintrag

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

Link zur Publikation (PDF, 1,9 MB) Download Bibtex Eintrag

Approximate inference for continuous–time Markov processes

Cédric Archambeau and Manfred Opper

Bayesian Time Series Models. Cambridge University Press, 125–140.. 2011

Link zur Publikation (PDF, 411,1 KB) Download Bibtex Eintrag

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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Postadresse

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