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Bioinformatics (Oxford, England)|May 7, 2010
Learning combinatorial transcriptional dynamics from gene expression dataManfred Opper, Guido SanguinettiNeural Computation|September 13, 2008
The variational gaussian approximation revisitedManfred Opper, Cédric ArchambeauJournal of Chemical Information and Modeling|November 13, 2023
MetalHawk: Enhanced Classification of Metal Coordination Geometries by Artificial Neural NetworksGianmattia Sgueglia, Michail D Vrettas, Marco Chino, et al.Entropy (Basel, Switzerland)|August 26, 2022
Stochastic Control for Bayesian Neural Network TrainingLudwig Winkler, César Ojeda, Manfred OpperNeural Computation|June 28, 2018
Optimal Decoding of Dynamic Stimuli by Heterogeneous Populations of Spiking Neurons: A Closed-Form ApproximationYuval Harel, Ron Meir, Manfred OpperEntropy (Basel, Switzerland)|December 8, 2020
Interacting Particle Solutions of Fokker-Planck Equations Through Gradient-Log-Density EstimationDimitra Maoutsa, Sebastian Reich, Manfred OpperEntropy (Basel, Switzerland)|February 25, 2023
A Score-Based Approach for Training Schrödinger Bridges for Data ModellingLudwig Winkler, Cesar Ojeda, Manfred OpperPhysical Review. E|September 27, 2018
Approximate Bayes learning of stochastic differential equationsPhilipp Batz, Andreas Ruttor, Manfred OpperEntropy (Basel, Switzerland)|March 25, 2022
Variational Bayesian Inference for Nonlinear Hawkes Process with Gaussian Process Self-EffectsNoa Malem-Shinitski, César Ojeda, Manfred OpperEntropy (Basel, Switzerland)|August 27, 2021
Flexible and Efficient Inference with Particles for the Variational Gaussian ApproximationThéo Galy-Fajou, Valerio Perrone, Manfred OpperPageof 3