Publications by authors named "Manfred Opper"

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A Score-Based Approach for Training Schrödinger Bridges for Data Modelling.
Ludwig Winkler, Cesar Ojeda, Manfred Opper

Entropy (Basel)· February 2023

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Stochastic Control for Bayesian Neural Network Training.
Ludwig Winkler, César Ojeda, Manfred Opper

Entropy (Basel)· August 2022

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Variational Bayesian Inference for Nonlinear Hawkes Process with Gaussian Process Self-Effects.
Noa Malem-Shinitski, César Ojeda, Manfred Opper

Entropy (Basel)· February 2022

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Flexible and Efficient Inference with Particles for the Variational Gaussian Approximation.
Théo Galy-Fajou, Valerio Perrone, Manfred Opper

Entropy (Basel)· July 2021

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Exact solution to the random sequential dynamics of a message passing algorithm.
Burak Çakmak, Manfred Opper

Phys Rev E· March 2021

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A mathematical model of local and global attention in natural scene viewing.
Noa Malem-Shinitski, Manfred Opper, Sebastian Reich, Lisa Schwetlick, Stefan A Seelig

PLoS Comput Biol· December 2020

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Interacting Particle Solutions of Fokker-Planck Equations Through Gradient-Log-Density Estimation.
Dimitra Maoutsa, Sebastian Reich, Manfred Opper

Entropy (Basel)· July 2020

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Memory-free dynamics for the Thouless-Anderson-Palmer equations of Ising models with arbitrary rotation-invariant ensembles of random coupling matrices.
Burak Çakmak, Manfred Opper

Phys Rev E· June 2019

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Optimal Decoding of Dynamic Stimuli by Heterogeneous Populations of Spiking Neurons: A Closed-Form Approximation.
Yuval Harel, Ron Meir, Manfred Opper

Neural Comput· August 2018

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Approximate Bayes learning of stochastic differential equations.
Philipp Batz, Andreas Ruttor, Manfred Opper

Phys Rev E· August 2018

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Publisher's Note: Inverse Ising problem in continuous time: A latent variable approach [Phys. Rev. E 96, 062104 (2017)].
Christian Donner, Manfred Opper

Phys Rev E· January 2018

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Inverse Ising problem in continuous time: A latent variable approach.
Christian Donner, Manfred Opper

Phys Rev E· December 2017

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Variational mean-field algorithm for efficient inference in large systems of stochastic differential equations.
Michail D Vrettas, Manfred Opper, Dan Cornford

Phys Rev E Stat Nonlin Soft Matter Phys· January 2015

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Common input explains higher-order correlations and entropy in a simple model of neural population activity.
Jakob H Macke, Manfred Opper, Matthias Bethge

Phys Rev Lett· May 2011

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Expectation propagation with factorizing distributions: a Gaussian approximation and performance results for simple models.
Fabiano Ribeiro, Manfred Opper

Neural Comput· April 2011

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Learning combinatorial transcriptional dynamics from gene expression data.
Manfred Opper, Guido Sanguinetti

Bioinformatics· July 2010

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Parameter estimation and inference for stochastic reaction-diffusion systems: application to morphogenesis in D. melanogaster.
Michael A Dewar, Visakan Kadirkamanathan, Manfred Opper, Guido Sanguinetti

BMC Syst Biol· March 2010

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Efficient statistical inference for stochastic reaction processes.
Andreas Ruttor, Manfred Opper

Phys Rev Lett· December 2009

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Switching regulatory models of cellular stress response.
Guido Sanguinetti, Andreas Ruttor, Manfred Opper, Cedric Archambeau

Bioinformatics· May 2009

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The variational gaussian approximation revisited.
Manfred Opper, Cédric Archambeau

Neural Comput· March 2009

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Statistical mechanics of learning: a variational approach for real data.
Dörthe Malzahn, Manfred Opper

Phys Rev Lett· September 2002

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Sparse on-line gaussian processes.
Lehel Csató, Manfred Opper

Neural Comput· March 2002

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