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Manfred Opper

Showing results (11-20 of 22) with videos related to

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Entropy (Basel, Switzerland)|August 26, 2022
Stochastic Control for Bayesian Neural Network TrainingLudwig Winkler, César Ojeda, Manfred Opper
Neural Computation|June 28, 2018
Optimal Decoding of Dynamic Stimuli by Heterogeneous Populations of Spiking Neurons: A Closed-Form ApproximationYuval Harel, Ron Meir, Manfred Opper
Entropy (Basel, Switzerland)|December 8, 2020
Interacting Particle Solutions of Fokker-Planck Equations Through Gradient-Log-Density EstimationDimitra Maoutsa, Sebastian Reich, Manfred Opper
Entropy (Basel, Switzerland)|February 25, 2023
A Score-Based Approach for Training Schrödinger Bridges for Data ModellingLudwig Winkler, Cesar Ojeda, Manfred Opper
Physical Review. E|September 27, 2018
Approximate Bayes learning of stochastic differential equationsPhilipp Batz, Andreas Ruttor, Manfred Opper
Entropy (Basel, Switzerland)|March 25, 2022
Variational Bayesian Inference for Nonlinear Hawkes Process with Gaussian Process Self-EffectsNoa Malem-Shinitski, César Ojeda, Manfred Opper
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|February 14, 2015
Variational mean-field algorithm for efficient inference in large systems of stochastic differential equationsMichail D Vrettas, Manfred Opper, Dan Cornford
Entropy (Basel, Switzerland)|August 27, 2021
Flexible and Efficient Inference with Particles for the Variational Gaussian ApproximationThéo Galy-Fajou, Valerio Perrone, Manfred Opper
Physical Review Letters|June 15, 2011
Common input explains higher-order correlations and entropy in a simple model of neural population activityJakob H Macke, Manfred Opper, Matthias Bethge
Bioinformatics (Oxford, England)|March 13, 2009
Switching regulatory models of cellular stress responseGuido Sanguinetti, Andreas Ruttor, Manfred Opper, et al.
Pageof 3

Showing results (11-20 of 22) with videos related to

Sort By:
Pageof 3
Entropy (Basel, Switzerland)|August 26, 2022
Stochastic Control for Bayesian Neural Network TrainingLudwig Winkler, César Ojeda, Manfred Opper
Neural Computation|June 28, 2018
Optimal Decoding of Dynamic Stimuli by Heterogeneous Populations of Spiking Neurons: A Closed-Form ApproximationYuval Harel, Ron Meir, Manfred Opper
Entropy (Basel, Switzerland)|December 8, 2020
Interacting Particle Solutions of Fokker-Planck Equations Through Gradient-Log-Density EstimationDimitra Maoutsa, Sebastian Reich, Manfred Opper
Entropy (Basel, Switzerland)|February 25, 2023
A Score-Based Approach for Training Schrödinger Bridges for Data ModellingLudwig Winkler, Cesar Ojeda, Manfred Opper
Physical Review. E|September 27, 2018
Approximate Bayes learning of stochastic differential equationsPhilipp Batz, Andreas Ruttor, Manfred Opper
Entropy (Basel, Switzerland)|March 25, 2022
Variational Bayesian Inference for Nonlinear Hawkes Process with Gaussian Process Self-EffectsNoa Malem-Shinitski, César Ojeda, Manfred Opper
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|February 14, 2015
Variational mean-field algorithm for efficient inference in large systems of stochastic differential equationsMichail D Vrettas, Manfred Opper, Dan Cornford
Entropy (Basel, Switzerland)|August 27, 2021
Flexible and Efficient Inference with Particles for the Variational Gaussian ApproximationThéo Galy-Fajou, Valerio Perrone, Manfred Opper
Physical Review Letters|June 15, 2011
Common input explains higher-order correlations and entropy in a simple model of neural population activityJakob H Macke, Manfred Opper, Matthias Bethge
Bioinformatics (Oxford, England)|March 13, 2009
Switching regulatory models of cellular stress responseGuido Sanguinetti, Andreas Ruttor, Manfred Opper, et al.
Pageof 3