M. Opper

发表

Calculation of the learning curve of Bayes optimal classification algorithm for learning a perceptron with noise

David Haussler, Manfred Opper, D. Haussler, 1991, COLT '91.

On the ability of the optimal perceptron to generalise

M. Opper, W. Kinzel, J. Kleinz, 1990 .

Worst Case Prediction over Sequences under Log Loss

D. Haussler, M. Opper, 1999 .

The Variational Gaussian Approximation Revisited

Manfred Opper, Cédric Archambeau, M. Opper, 2009, Neural Computation.

Comparing the Mean Field Method and Belief Propagation for Approximate Inference in MRFs

M. Opper, David Saad, D. Saad, 2001 .

Advanced mean field methods: theory and practice

M. Opper, David Saad, D. Saad, 2001 .

An Idiosyncratic Journey Beyond Mean Field Theory

M. Opper, David Saad, 2001 .

MUTUAL INFORMATION, METRIC ENTROPY, AND RISK IN ESTIMATION OF PROBABILITY DISTRIBUTIONS

D. Haussler, M. Opper, 1996 .

Perceptron learning: the largest version space

M. Opper, M. Biehl, 1995 .

Some Examples of Recursive Variational Approximations for Bayesian Inference

M. Opper, David Saad, 2001 .

Sparse Online Gaussian Processes

M. Opper, 2008 .

Gaussian processes and SVM: Mean field results and leave-one-out

M. Opper, O. Winther, 2000 .

Optimal control as a graphical model inference problem

Vicenç Gómez, Hilbert J. Kappen, Manfred Opper, 2009, Machine Learning.

Distribution of Internal Fields and Dynamics of Neural Networks

M. Opper, R. Henkel, 1990 .

Eecient Approaches to Gaussian Process Classiication

M. Opper, Ernest Fokou, Bernhard SchottkyNeural, 2000 .

Tractable Approximate Belief Propagation

M. Opper, David Saad, 2001 .

Gaussian Process Approximations of Stochastic Differential Equations

Dan Cornford, John Shawe-Taylor, Manfred Opper, 2007, Gaussian Processes in Practice.

Query by committee

H. Sebastian Seung, Manfred Opper, Haim Sompolinsky, 1992, COLT '92.

Tractable approximations for probabilistic models: the adaptive Thouless-Anderson-Palmer mean field approach.

M. Opper, O. Winther, 2001, Physical review letters.

Deterministic particle flows for constraining SDEs

Manfred Opper, Dimitra Maoutsa, M. Opper, 2021, 2110.13020.

Dynamics of batch training in a perceptron

M. Opper, S. Bös, 1998 .

Adaptive and self-averaging Thouless-Anderson-Palmer mean-field theory for probabilistic modeling.

M. Opper, O. Winther, 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

Sparse gaussian processes: inference, subspace identification and model selection

M. Opper, L. Csató, 2003 .

Sparse On-Line Gaussian Processes

Lehel Csató, Manfred Opper, M. Opper, 2002, Neural Computation.

Information Geometry of Mean Field Approximation

M. Opper, David Saad, D. Saad, 2001 .

From Naive Mean Field Theory to the TAP Equations

M. Opper, David Saad, 2001 .

On the annealed VC entropy for margin classifiers: a statistical mechanics study

M. Opper, 1999 .

Statistical mechanics of Support Vector networks.

M. Opper, H. Sompolinsky, R. Dietrich, 1998, cond-mat/9811421.

Learning in Neural Networks: Solvable Dynamics

M. Opper, 1989 .

Statistical Mechanics of Learning : Generalization

M. Opper, 2002 .

The effect of correlations in neural networks

M. Opper, W. Kinzel, A. Wendemuth, 1993 .

5 Statistical Mechanics of Generalization

M. Opper, W. Kinzel, .

Gaussian Process Classiication and Svm: Mean Field Results and Leave-one-out Estimator

M. Opper, O. Winther, 2007 .

Gaussian Processes for Classification: Mean-Field Algorithms

Ole Winther, Manfred Opper, M. Opper, 2000, Neural Computation.

General bounds on the mutual information between a parameter and n conditionally independent observations

David Haussler, Manfred Opper, D. Haussler, 1995, COLT '95.

Mutual Information and Bayes Methods for Learning a Distribution

M. Opper, 1995 .

GENERAL BOUNDS FOR PREDICTIVE ERRORS IN SUPERVISED LEARNING

M. Opper, 1995 .

Sparse Representation for Gaussian Process Models

Manfred Opper, Lehel Csató, M. Opper, 2000, NIPS.

Variational Inference for Stochastic Differential Equations

M. Opper, 2019, Annalen der Physik.

Regret Bounds for Gaussian Process Bandit Problems

John Shawe-Taylor, Steffen Grünewälder, Jean-Yves Audibert, 2010, AISTATS 2010.

TAP Gibbs Free Energy, Belief Propagation and Sparsity

Ole Winther, Manfred Opper, Lehel Csató, 2001, NIPS.

Regression with Gaussian Processes: Average Case Performance

M. Opper, 2006 .

Information Geometry of α-Projection in Mean Field Approximation

M. Opper, David Saad, 2001 .

Advances in Neural Information Processing Systems 20 (NIPS 2007)

Botond Cseke, Manfred Opper, Guido Sanguinetti, 2008 .

A Bayesian approach to on-line learning

Manfred Opper, M. Opper, 1999 .

Perturbation Theory for Variational Inference

Alex K. Susemihl, M. Opper, Marco Fraccaro, 2015 .

Bayesian Inference for Change Points in Dynamical Systems with Reusable States - a Chinese Restaurant Process Approach

Andreas Ruttor, Manfred Opper, Florian Stimberg, 2012, AISTATS.

Approximate inference techniques with expectation constraints

W. Wiegerinck, T. Heskes, M. Opper, 2005 .

Common input explains higher-order correlations and entropy in a simple model of neural population activity.

M. Bethge, M. Opper, J. Macke, 2011, Physical review letters.

Optimal decoding of dynamic stimuli encoded by heterogeneous populations of spiking neurons - a closed form approximation

M. Opper, R. Meir, Y. Harel, 2019, 1901.04094.

An Analytically Tractable Bayesian Approximation to Optimal Point Process Filtering

M. Opper, R. Meir, Y. Harel, 2015, 1507.07813.

Expectation Propagation

M. Opper, Jack Raymond, Andre Manoel, 2014, 1409.6179.

Bayes-optimal performance in a discrete space

C. Broeck, M. Opper, M. Copelli, 1999 .

Interacting Particle Solutions of Fokker–Planck Equations Through Gradient–Log–Density Estimation

M. Opper, S. Reich, Dimitra Maoutsa, 2020, Entropy.

Inferring hidden states in Langevin dynamics on large networks: Average case performance.

M. Opper, Peter Sollich, B. Bravi, 2016, Physical review. E.

An estimator for the relative entropy rate of path measures for stochastic differential equations

M. Opper, 2017, J. Comput. Phys..

Bayesian Time Series Models: Approximate inference for continuous-time Markov processes

M. Opper, C. Archambeau, 2011 .

Chapter 1 Approximate inference for continuous-time Markov processes

M. Opper, C. Archambeau, 2009 .

Critical storage capacity of the J = ± 1 neural network

Werner Krauth, M Opper, W. Krauth, 1989 .

Online Learning of Wind-Field Models

M. Opper, D. Cornford, L. Csató, 2001 .

Supervised Learning: Information Theoretic Bounds on predictive errors

M. Opper, 2007 .

Parallel dynamics of the neural network with the pseudoinverse coupling matrix

R D Henkel, M Opper, M. Opper, 1991 .

Dicussion on the meeting on ‘Statistical approaches to inverse problems’

Simon J. Godsill, Dan Cornford, Eric Moulines, 2004 .

Sequential, Bayesian Geostatistics: A Principled Method for Large Data Sets

Dan Cornford, Manfred Opper, Lehel Csató, 2005 .

Approximate Bayes learning of stochastic differential equations.

Andreas Ruttor, Manfred Opper, Philipp Batz, 2017, Physical review. E.

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

M. Opper, Andreas Ruttor, Philipp Batz, 2016, 1603.01159.

A Mean Eld Algorithm for Bayes Learning in Large Feed-forward Neural Networks

Ole Winther, Manfred Opper, M. Opper, 1997 .

Expectation propagation for continuous time stochastic processes

Guido Sanguinetti, Manfred Opper, Botond Cseke, 2015, 1512.06098.

A basis function approach to Bayesian inference in diffusion processes

M. Opper, D. Cornford, Y. Shen, 2009, 2009 IEEE/SP 15th Workshop on Statistical Signal Processing.

Maximal stability in unsupervised learning

M. Opper, W. Kinzel, A. Mietzner, 1995 .

Inverse Ising problem in continuous time: A latent variable approach.

Manfred Opper, Christian Donner, M. Opper, 2017, Physical review. E.

Approximate Gaussian process inference for the drift of stochastic differential equations

Andreas Ruttor, Manfred Opper, Philipp Batz, 2013, NIPS 2013.

Probabilistic data modelling with adaptive TAP mean-field theory

M. Opper, O. Winther, 2001 .

Sequential, Sparse Learning in Gaussian Processes

M. Opper, D. Cornford, L. Csató, 2022 .

On the relaxation of infinite-range spin glasses

M. Opper, W. Kinzel, A. Scharnagl, 1995 .

Analysis of Random Sequential Message Passing Algorithms for Approximate Inference

Yue M. Lu, M. Opper, Burak Çakmak, 2022, ArXiv.

MUTUAL INFORMATION, METRIC ENTROPY AND CUMULATIVE RELATIVE ENTROPY RISK

D. Haussler, M. Opper, 1997 .

Eecient Approaches to Gaussian Process Classiication

M. Opper, Ernest Fokou, Bernhard SchottkyNeural, 2000 .

Solution of a random chain problem: an approach using canonical variables of an integrable system

M. Opper, 1986 .

Convexity, internal representations and the statistical mechanics of neural networks

M. Opper, A. Mietzner, P. Kuhlmann, 1997 .

Dynamics of learning

M. Opper, W. Kinzel, 1991 .

Variational Inference in Stochastic Dynamic Environmental Models

J. Shawe-Taylor, M. Opper, D. Cornford, 2005 .

Tractable approximations for probabilistic models: the adaptive Thouless-Anderson-Palmer mean field approach.

M. Opper, O. Winther, 2001, Physical review letters.

Statistical mechanics of Support Vector networks.

M. Opper, H. Sompolinsky, R. Dietrich, 1998, cond-mat/9811421.

Deterministic particle flows for constraining stochastic nonlinear systems

M. Opper, Dimitra Maoutsa, 2021, Physical Review Research.

Supplementary details and explanations for paper “ A model of neural population activity in the presence of common input explains higher-order correlations and entropy ”

M. Bethge, M. Opper, J. Macke, 2014 .

Statistical mechanics of learning: a variational approach for real data.

M. Opper, D. Malzahn, 2002, Physical review letters.

Stochastic Control for Bayesian Neural Network Training

M. Opper, C. Ojeda, Ludwig Winkler, 2022, Entropy.

Statistical mechanics of learning in the presence of outliers

M. Opper, R. Dietrich, 1998, cond-mat/9803316.

Analytical solution of the classical Bethe-ansatz equation for the Toda chain

M. Opper, 1985 .

Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions

M. Opper, C. Archambeau, D. Cornford, 2011, Computational Statistics.

Storage of correlated patterns in a perceptron

M. Opper, B. López, M. Schroeder, 1995, cond-mat/9505093.

Bayes-optimal performance in a discrete space

C. Broeck, M. Opper, M. Copelli, 1999, cond-mat/9906356.

Advances in Neural Information Processing Systems 24

Guido Sanguinetti, Botond Cseke, Manfred Opper, 2011 .

inverse problems: some new approaches

M. Opper, D. Cornford, L. Csató, 2022 .

Deterministic particle flows for constraining SDEs

M. Opper, Dimitra Maoutsa, 2021, 2110.13020.

Perturbation Corrections in Approximate Inference: Mixture Modelling Applications

Ole Winther, Manfred Opper, Ulrich Paquet, 2009, J. Mach. Learn. Res..

Variational Inference for Diffusion Processes

Dan Cornford, Yuan Shen, John Shawe-Taylor, 2007, NIPS.

Data Assimilation with Sequential Gaussian Processes

Dan Cornford, Manfred Opper, Lehel Csató, 2002 .

Online Approximations for Wind-Field Models

Dan Cornford, Manfred Opper, Lehel Csató, 2001, ICANN.

Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions

Dan Cornford, Yuan Shen, Manfred Opper, 2012, Comput. Stat..

Construction algorithm for the parity-machine

Michael Biehl, Manfred Opper, M. Opper, 1993 .

A mathematical model of local and global attention in natural scene viewing

M. Opper, S. Reich, Ralf Engbert, 2020, PLoS Comput. Biol..

A Mathematical Model of Exploration and Exploitation in Natural Scene Viewing

M. Opper, S. Reich, Ralf Engbert, 2020, bioRxiv.

Learning combinatorial transcriptional dynamics from gene expression data

Guido Sanguinetti, Manfred Opper, M. Opper, 2010, Bioinform..

Visualizing the effects of a changing distance on data using continuous embeddings

Manfred Opper, Simon Barthelmé, Gina Gruenhage, 2013, Comput. Stat. Data Anal..

Expectation Propagation for Approximate Inference: Free Probability Framework

Manfred Opper, Burak Çakmak, M. Opper, 2018, 2018 IEEE International Symposium on Information Theory (ISIT).

Metric Entropy and Minimax Risk in Classification

David Haussler, Manfred Opper, D. Haussler, 1997, Structures in Logic and Computer Science.

Analytical Results for the Error in Filtering of Gaussian Processes

Alex K. Susemihl, Ron Meir, Manfred Opper, 2011, NIPS.

Region growing with pulse-coupled neural networks: an alternative to seeded region growing

Manfred Opper, Iris Fermin, Robert D. Stewart, 2002, IEEE Trans. Neural Networks.

Estimating parameters in stochastic systems: A variational Bayesian approach

Michail D. Vrettas, M. Opper, D. Cornford, 2011 .

Perturbative Black Box Variational Inference pdf

Manfred Opper, Cheng Zhang, Stephan Mandt, 2017, NIPS.

DARA: Estimating the behavior of data rate adaptation algorithms in WLAN hotspots

Andreas Ruttor, Adam Wolisz, Manfred Opper, 2013, 2013 Proceedings IEEE INFOCOM.

Solitons in the statistical mechanics of the Toda lattice

M. Opper, M. Opper, H. Bolterauer, 1981 .

Adaptive and Selfaveraging Thouless-Anderson-Palmer Mean Field Theory for Probabilistic Modelling

Manfred Opper, M. Opper, 2006 .

Inferring the collective dynamics of neuronal populations from single-trial spike trains using mechanistic models

Manfred Opper, Josef Ladenbauer, Christian Donner, 2019, bioRxiv.

Gaussian Density Parametrization Flow: Particle and Stochastic Approaches

M. Opper, Valerio Perrone, Théo Galy-Fajou, 2021 .

Simulating infinite systems

M. Opper, 1993 .

Gaussian Process Approximations of

J. Shawe-Taylor, M. Opper, C. Archambeau, 2007 .

Statistical mechanics of generalization

Manfred Opper, M. Opper, 1998 .

Basins of attraction near the critical storage capacity for neural networks with constant stabilities

Wolfgang Kinzel, M Opper, M. Opper, 1989 .

Expectation Consistent Approximate Inference

Ole Winther, Manfred Opper, M. Opper, 2005, J. Mach. Learn. Res..

Variational mean-field algorithm for efficient inference in large systems of stochastic differential equations.

Dan Cornford, Manfred Opper, Michail D. Vrettas, 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation pdf

Marius Kloft, Florian Wenzel, Manfred Opper, 2018, AAAI.

A dynamical mean-field theory for learning in restricted Boltzmann machines

M. Opper, Burak Çakmak, 2020, Journal of Statistical Mechanics: Theory and Experiment.

Analysis of Bayesian inference algorithms by the dynamical functional approach pdf

Manfred Opper, Burak Çakmak, M. Opper, 2020, Journal of Physics A: Mathematical and Theoretical.

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

Manfred Opper, Ludovica Bachschmid-Romano, M. Opper, 2017, 1705.05403.

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

Manfred Opper, Ludovica Bachschmid-Romano, M. Opper, 2015, 1508.05865.

Inferring hidden states in a random kinetic Ising model: replica analysis

Manfred Opper, Ludovica Bachschmid Romano, M. Opper, 2014, 1405.4164.

The effect of pairwise neural correlations on global population statistics

Matthias Bethge, Manfred Opper, Jakob H. Macke, 2009 .

A statistical physics approach for the analysis of machine learning algorithms on real data

Dörthe Malzahn, Manfred Opper, M. Opper, 2005 .

Learning curves and bootstrap estimates for inference with Gaussian processes: A statistical mechanics study

Manfred Opper, Dörthe Malzahn, M. Opper, 2003, Complex..

The Complexity of Learning with Supportvector Machines - A Statistical Physics Study

Manfred Opper, M. Opper, 2003 .

A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages

Manfred Opper, Dörthe Malzahn, M. Opper, 2002, NIPS.

A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding

Ron Meir, Manfred Opper, Yuval Harel, 2015, NIPS.

A theory of solving TAP equations for Ising models with general invariant random matrices pdf

Ole Winther, Manfred Opper, Burak Çakmak, 2015, ArXiv.

Optimal Neural Codes for Control and Estimation

Alex K. Susemihl, Ron Meir, Manfred Opper, 2014, NIPS.

Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes

Manfred Opper, Christian Donner, M. Opper, 2018, J. Mach. Learn. Res..

Approximate Gaussian process inference for the drift function in stochastic differential equations

Andreas Ruttor, Manfred Opper, Philipp Batz, 2013, NIPS.

Advances in Neural Information Processing Systems 26 (NIPS 2013)

M. Opper, G. Sanguinetti, Botond Cseke, 2013 .

Dynamics of Training

Manfred Opper, Siegfried Bös, M. Opper, 1996, NIPS.

Support vector machines learning noisy polynomial rules

M. Opper, R. Urbanczik, 2001 .

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

Manfred Opper, Burak Çakmak, M. Opper, 2019, Physical review. E.

Variational inference for Markov jump processes

Guido Sanguinetti, Manfred Opper, M. Opper, 2007, NIPS.

Convergent Dynamics for Solving the TAP Equations of Ising Models with Arbitrary Rotation Invariant Coupling Matrices

Manfred Opper, Burak Çakmak, M. Opper, 2019, 2019 IEEE International Symposium on Information Theory (ISIT).

Dynamical functional theory for compressed sensing

Ole Winther, Manfred Opper, Bernard H. Fleury, 2017, 2017 IEEE International Symposium on Information Theory (ISIT).

Efficient Approaches to Gaussian Process Classification

Ole Winther, Manfred Opper, Ernest Fokoué, 1999, NIPS.

Efficient statistical inference for stochastic reaction processes.

Andreas Ruttor, Manfred Opper, M. Opper, 2009, Physical review letters.

A Variational Approach to Learning Curves

Manfred Opper, Dörthe Malzahn, M. Opper, 2001, NIPS.

General Bounds on Bayes Errors for Regression with Gaussian Processes

Manfred Opper, Francesco Vivarelli, M. Opper, 1998, NIPS.

Learning Curves for Gaussian Processes Regression: A Framework for Good Approximations

Manfred Opper, Dörthe Malzahn, M. Opper, 2000, NIPS.

Inference in Linear Observations with Multiple Signal Sources: Analysis of Approximate Message Passing and Applications to Unsourced Random Access in Cell-Free Systems

M. Opper, G. Caire, Burak cCakmak, 2023, arXiv.org.

Tightening bounds for variational inference by revisiting perturbation theory

M. Opper, S. Mandt, Robert Bamler, 2019, Journal of Statistical Mechanics: Theory and Experiment.

Expectation Consistent Free Energies for Approximate Inference

Ole Winther, Manfred Opper, M. Opper, 2004, NIPS.

Advances in large margin classifiers

Manfred Opper, Haim Sompolinsky, Rainer Dietrich, 2000 .

Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation pdf

Florian Wenzel, Manfred Opper, Christian Donner, 2019, UAI.

Extended Plefka expansion for stochastic dynamics

Manfred Opper, Peter Sollich, Barbara Bravi, 2015, 1509.07066.

Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models

Florian Wenzel, Manfred Opper, Th'eo Galy-Fajou, 2020, AISTATS.

Nonlinear Hawkes Process with Gaussian Process Self Effects pdf

Manfred Opper, Noa Malem-Shinitski, Cesar Ojeda, 2021, ArXiv.

Statistical physics of learning and inference

Thomas Villmann, Michael Biehl, Manfred Opper, 2019, ESANN.

Efficient Bayesian Inference for a Gaussian Process Density Model pdf

Manfred Opper, Christian Donner, M. Opper, 2018, UAI.

Dynamic state estimation based on Poisson spike trains—towards a theory of optimal encoding

Alex K. Susemihl, Ron Meir, Manfred Opper, 2012, 1209.5559.

Drifting Games and Brownian Motion

Yoav Freund, Manfred Opper, Y. Freund, 2002, J. Comput. Syst. Sci..

Approximate inference in latent Gaussian-Markov models from continuous time observations

Guido Sanguinetti, Manfred Opper, Botond Cseke, 2013, NIPS.

Regret Bounds for Gaussian Process Bandit Problems

Jean-Yves Audibert, J. Shawe-Taylor, M. Opper, 2010, International Conference on Artificial Intelligence and Statistics.

Optimal Decoding of Dynamic Stimuli by Heterogeneous Populations of Spiking Neurons: A Closed-Form Approximation

Ron Meir, Manfred Opper, Yuval Harel, 2018, Neural Computation.

Tutorial on Variational Approximation Methods

M. Opper, David Saad, 2001 .

Regret Bounds for Gaussian Process Bandit Problems

Jean-Yves Audibert, J. Shawe-Taylor, M. Opper, 2010, AISTATS.

A Comparison of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems

Dan Cornford, Yuan Shen, John Shawe-Taylor, 2007, J. Signal Process. Syst..

Evaluation of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems

J. Shawe-Taylor, Y. Shen, C. Archambeau, 2007, 2007 IEEE Workshop on Machine Learning for Signal Processing.

Flexible Temporal Point Processes Modeling with Nonlinear Hawkes Processes with Gaussian Processes Excitations and Inhibitions

M. Opper, C. Ojeda, Noa Malem-Shinitski, 2021 .

GP-ETAS: semiparametric Bayesian inference for the spatio-temporal epidemic type aftershock sequence model

Sebastian Reich, Matthias Holschneider, Manfred Opper, 2020, Statistics and Computing.

Expectation Propagation with Factorizing Distributions: A Gaussian Approximation and Performance Results for Simple Models

Manfred Opper, Fabiano Ribeiro, M. Opper, 2011, Neural Computation.

Mean Field Methods for Classification with Gaussian Processes

Ole Winther, Manfred Opper, M. Opper, 1998, NIPS.

Flexible and Efficient Inference with Particles for the Variational Gaussian Approximation

Manfred Opper, Valerio Perrone, Théo Galy-Fajou, 2021, Entropy.

Retarded learning: rigorous results from statistical mechanics.

M Opper, M. Opper, D. Herschkowitz, 2001, Physical review letters.

Statistical mechanics of the knapsack problem

M. Opper, E. Korutcheva, M. Opper, 1994 .

Learning algorithm for a neural network with binary synapses

Wolfgang Kinzel, S. Diederich, M. Opper, 1990 .

Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data

Andreas Ruttor, Manfred Opper, Florian Stimberg, 2014, NIPS.

A Bayesian Approach to Online Learning

Manfred Opper, M. Opper, 2006 .

The quantum lifetime of the Davydov soliton

M. Opper, H. Bolterauer, 1991 .

A mathematical model of local and global attention in natural scene viewing

M. Opper, S. Reich, Ralf Engbert, 2020, PLoS Comput. Biol..

A new variational radial basis function approximation for inference in multivariate diffusions

Dan Cornford, Yuan Shen, Manfred Opper, 2010, Neurocomputing.