Manfred Opper

8.8k citations

177 papers · 6.1k · 3 hit papers · h-index 33

Impact in

Artificial Intelligence top 0.2%
- Neural Networks and Applications
- Machine Learning and Algorithms
- Gaussian Processes and Bayesian Inference
- Reinforcement Learning in Robotics
Statistics and Probability top 0.5%
- Markov Chains and Monte Carlo Methods

Papers in

Artificial Intelligence 127
- Neural Networks and Applications 56
- Gaussian Processes and Bayesian Inference 55
- Machine Learning and Algorithms 23
- Bayesian Modeling and Causal Inference 15
- Bayesian Methods and Mixture Models 7
Statistics and Probability 31
- Markov Chains and Monte Carlo Methods 22

Co-authors: Haim Sompolinsky (4 shared papers)H. Sebastian Seung (1 shared paper)Ole Winther (23 shared papers)Lehel Csató (6 shared papers)Sigurd Diederich (5 shared papers)Guido Sanguinetti (10 shared papers)David H. Haussler (10 shared papers)Botond Cseke (2 shared papers)
Journals: Physical Review Letters (15 papers)Neural Computation (5 papers)Physical review. E (5 papers)Physical Review A (4 papers)Europhysics Letters (EPL) (4 papers)
Partner nations: Germany United Kingdom Denmark

In The Last Decade

Manfred Opper

173 papers receiving 5.8k citations

Manfred Opper's Hit Papers

align trajectories log scale

Advances in Neural Information Processing Systems 26 (NIPS 2013) 2013 · 377 citations

What are hit papers?

Paper score: A paper's citations measured against the top-1% bar of its own field and year: the square root of citations ÷ bar, so 1 means right at the bar and 2 means four times its citations. The bar blends the citation counts that enter the top 1% of the paper's subfields in its year (weighted 89.5%), of those subfields over all years (0.5%) and of its year across all fields (10%); a subfield and year with fewer than 400 papers takes its year's bar. Papers from 2026 are not scored yet: in an unfinished year, when a paper appeared matters more than how it is cited.

Hit paper: A paper cited at least 1.5× the top-1% bar of its field and year: a paper score of at least √1.5.

2013 Advances in Neural Information Processing Systems 26 (NIPS 2013)
2002 Neural Computation
1992 Query by committee

Peers

Countries citing papers authored by Manfred Opper

Since Specialization

Citations

This map shows the geographic impact of Manfred Opper's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Manfred Opper with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Manfred Opper more than expected).

Fields of papers citing papers by Manfred Opper

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Manfred Opper. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Manfred Opper. The network helps show where Manfred Opper may publish in the future.

Co-authors

The 25 scholars most cited alongside Manfred Opper, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Manfred Opper Line = papers co-authored together Manfred Opper links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

Showing the 20 most-cited of 177 papers — load more, or switch the sort, to bring in the rest.

#	Work
1	Query by committee H. Sebastian Seung, Manfred Opper, Haim Sompolinsky Hit paper breakdown →	1992	1271
2	Sparse On-Line Gaussian Processes Neural Computation ·Lehel Csató, Manfred Opper Hit paper breakdown →	2002	557
3	Advances in Neural Information Processing Systems 26 (NIPS 2013) Botond Cseke, Manfred Opper, Guido Sanguinetti Hit paper breakdown →	2013	377
4	Advances in Neural Information Processing Systems 11 (NIPS 1998) Giancarlo Ferrari-trecate A, Christopher K. I. Williams, Manfred Opper	1999	207
5	Learning of correlated patterns in spin-glass networks by local learning rules Physical Review Letters ·Sigurd Diederich, Manfred Opper	1987	202
6	Optimal control as a graphical model inference problem Machine Learning ·Hilbert J. Kappen, Vicenç Gómez, Manfred Opper	2012	179
7	The Variational Gaussian Approximation Revisited Neural Computation ·Manfred Opper, Cédric Archambeau	2008	163
8	Gaussian Processes for Classification: Mean-Field Algorithms Neural Computation ·Manfred Opper, Ole Winther	2000	151
9	Generalization performance of Bayes optimal classification algorithm for learning a perceptron Physical Review Letters ·Manfred Opper, David H. Haussler	1991	120
10	Expectation Consistent Approximate Inference Journal of Machine Learning Research ·Manfred Opper, Ole Winther	2005	107
11	Statistical Mechanics of Support Vector Networks Physical Review Letters ·Manfred Opper, Haim Sompolinsky +1 other	1999	94
12	On the ability of the optimal perceptron to generalise Journal of Physics A Mathematical and General ·Manfred Opper, Wolfgang Kinzel, J Kleinz, R Nehl	1990	91
13	Mutual information, metric entropy and cumulative relative entropy risk The Annals of Statistics ·David H. Haussler, Manfred Opper	1997	90
14	Adaptive and self-averaging Thouless-Anderson-Palmer mean-field theory for probabilistic modeling Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics ·Manfred Opper, Ole Winther	2001	89
15	New method for studying the dynamics of disordered spin systems without finite-size effects Physical Review Letters ·H. Eissfeller, Manfred Opper	1992	88
16	Phase transition and 1/fnoise in a game dynamical model Physical Review Letters ·Manfred Opper, Sigurd Diederich	1992	77
17	Region growing with pulse-coupled neural networks: an alternative to seeded region growing IEEE Transactions on Neural Networks ·Iris Fermin, Manfred Opper +1 other	2002	77
18	Replicators with random interactions: A solvable model Physical Review A ·Sigurd Diederich, Manfred Opper	1989	74
19	Common Input Explains Higher-Order Correlations and Entropy in a Simple Model of Neural Population Activity Physical Review Letters ·Jakob H. Macke, Manfred Opper, Matthias Bethge	2011	72
20	A Bayesian Approach to On-line Learning Cambridge University Press eBooks ·Manfred Opper	1999	67

About Manfred Opper

Manfred Opper is a scholar working on Artificial Intelligence, Statistics and Probability, Condensed Matter Physics, Statistical and Nonlinear Physics and Mathematical Physics, having authored 177 papers that have together received 6.1k indexed citations. Recurring topics across this work include Neural Networks and Applications (56 papers), Gaussian Processes and Bayesian Inference (55 papers), Machine Learning and Algorithms (23 papers), Theoretical and Computational Physics (23 papers), Markov Chains and Monte Carlo Methods (22 papers), Bayesian Modeling and Causal Inference (15 papers), Neural dynamics and brain function (9 papers) and Bayesian Methods and Mixture Models (7 papers). The work is most often cited by research in Artificial Intelligence (3.5k citations), Statistics and Probability (473 citations), Statistical and Nonlinear Physics (281 citations), Condensed Matter Physics (294 citations) and Computer Vision and Pattern Recognition (480 citations). Manfred Opper has collaborated with scholars based in Germany, United Kingdom and Denmark. Frequent co-authors include Haim Sompolinsky, H. Sebastian Seung, Ole Winther, Lehel Csató, Sigurd Diederich, Guido Sanguinetti, David H. Haussler, Botond Cseke, Cédric Archambeau and Wolfgang Kinzel. Their work appears in journals such as Physical Review Letters, Neural Computation, Physical review. E, Physical Review A and Europhysics Letters (EPL).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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Manfred Opper

Manfred Opper's Hit Papers

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All Works

About Manfred Opper

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