direkt zum Inhalt springen

direkt zum Hauptnavigationsmenü

Sie sind hier

TU Berlin

Page Content

List of Publications

B

Resonant and Quasiclassical Excitations of Solitons in the Alpha- Helix

Bolterauer, H., Henkel, R. D. and Opper, M.

Structure, Coherence and Chaos in Dynamical Systems. Manchester University Press, 625–631. 1986

Download Bibtex entry

Construction Algorithm for the Parity Machine

Biehl, M. and Opper, M.

Physica A, 307–313. 1993

Download Bibtex entry

Tilinglike Learning in the Parity Machine

Biehl, M. and Opper, M.

Phys. Rev. A, 6888–6894. 1991

Download Bibtex entry

Perceptron Learning: The Largest Version Space

Biehl, M. and Opper, M.

Theory of Neural Networks, The Statistical Mechanics Perspective. World Scientific, 59-72. 1995

Link to publication Download Bibtex entry




Variational estimation of the drift for stochastic differential equations from the empirical density

Batz, P., Ruttor, A. and Opper, M.

Journal of Statistical Mechanics: Theory and Experiment, 083404. 2016

Link to publication Download Bibtex entry

Approximate Bayes learning of stochastic differential equations

Batz, P., Ruttor, A. and Opper, M.

Phys. Rev. E, 022109. 2018

Download Bibtex entry

Perturbative Black Box Corrected Variational Inference

Bamler R., C. Z. O. M. M. S.

Advances in Neural Information Processing Systems 30. IEEE, 11. 2017

Link to publication Download Bibtex entry

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

Bachschmid-Romano, L. and Opper, M.

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

Download Bibtex entry

A statistical physics approach to learning curves for the inverse Ising problem

Bachschmid-Romano, L. and Opper, M.

Journal of Statistical Mechanics: Theory and Experiment, 063406. 2017

Link to publication Download Bibtex entry

A

Variational Inference for Diffusion Processes

Archambeau, C., Opper, M., Shen, Y., Cornford, D. and Shawe-Taylor, J.

Advances in Neural Information Processing Systems 20. MIT Press, 17–24. 2008

Download Bibtex entry

Gaussian Process Approximations of Stochastic Differential Equations

Archambeau, C., Cornford, D., Opper, M. and Shawe-Taylor, J.

Journal of Machine Learning Research: Workshop and Conference Proceedings, 1:1–16. 2007

Link to publication Download Bibtex entry

Approximate inference for continuous–time Markov processes

Archambeau, C. and Opper, M.

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

Link to publication Download Bibtex entry

Zusatzinformationen / Extras

Quick Access:

Schnellnavigation zur Seite über Nummerneingabe

Auxiliary Functions