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Items: 41

1.
2.

Approximate Bayes learning of stochastic differential equations.

Batz P, Ruttor A, Opper M.

Phys Rev E. 2018 Aug;98(2-1):022109. doi: 10.1103/PhysRevE.98.022109.

PMID:
30253603
3.

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

Harel Y, Meir R, Opper M.

Neural Comput. 2018 Aug;30(8):2056-2112. doi: 10.1162/neco_a_01105. Epub 2018 Jun 27.

PMID:
29949463
4.

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

Donner C, Opper M.

Phys Rev E. 2018 Jan;97(1-2):019901. doi: 10.1103/PhysRevE.97.019901.

PMID:
29448461
5.

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

Donner C, Opper M.

Phys Rev E. 2017 Dec;96(6-1):062104. doi: 10.1103/PhysRevE.96.062104. Epub 2017 Dec 4. Erratum in: Phys Rev E. 2018 Jan;97(1-2):019901.

PMID:
29347355
6.

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

Bravi B, Opper M, Sollich P.

Phys Rev E. 2017 Jan;95(1-1):012122. doi: 10.1103/PhysRevE.95.012122. Epub 2017 Jan 13.

PMID:
28208380
7.

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

Vrettas MD, Opper M, Cornford D.

Phys Rev E Stat Nonlin Soft Matter Phys. 2015 Jan;91(1):012148. Epub 2015 Jan 30.

PMID:
25679611
8.

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

Macke JH, Opper M, Bethge M.

Phys Rev Lett. 2011 May 20;106(20):208102. Epub 2011 May 17.

PMID:
21668265
9.

Expectation propagation with factorizing distributions: a Gaussian approximation and performance results for simple models.

Ribeiro F, Opper M.

Neural Comput. 2011 Apr;23(4):1047-69. doi: 10.1162/NECO_a_00104. Epub 2011 Jan 11.

PMID:
21222527
10.

Learning combinatorial transcriptional dynamics from gene expression data.

Opper M, Sanguinetti G.

Bioinformatics. 2010 Jul 1;26(13):1623-9. doi: 10.1093/bioinformatics/btq244. Epub 2010 May 5.

PMID:
20444835
11.

Efficient statistical inference for stochastic reaction processes.

Ruttor A, Opper M.

Phys Rev Lett. 2009 Dec 4;103(23):230601. Epub 2009 Dec 2.

PMID:
20366136
12.

Parameter estimation and inference for stochastic reaction-diffusion systems: application to morphogenesis in D. melanogaster.

Dewar MA, Kadirkamanathan V, Opper M, Sanguinetti G.

BMC Syst Biol. 2010 Mar 10;4:21. doi: 10.1186/1752-0509-4-21.

13.

Switching regulatory models of cellular stress response.

Sanguinetti G, Ruttor A, Opper M, Archambeau C.

Bioinformatics. 2009 May 15;25(10):1280-6. doi: 10.1093/bioinformatics/btp138. Epub 2009 Mar 11.

PMID:
19279066
14.

The variational gaussian approximation revisited.

Opper M, Archambeau C.

Neural Comput. 2009 Mar;21(3):786-92. doi: 10.1162/neco.2008.08-07-592.

PMID:
18785854
15.

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

Stewart RD, Fermin I, Opper M.

IEEE Trans Neural Netw. 2002;13(6):1557-62. doi: 10.1109/TNN.2002.804229.

PMID:
18244552
16.

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

Malzahn D, Opper M.

Phys Rev Lett. 2002 Sep 2;89(10):108302. Epub 2002 Aug 19.

PMID:
12225232
17.

Sparse on-line gaussian processes.

Csató L, Opper M.

Neural Comput. 2002 Mar;14(3):641-68.

PMID:
11860686
18.

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

Opper M, Winther O.

Phys Rev E Stat Nonlin Soft Matter Phys. 2001 Nov;64(5 Pt 2):056131. Epub 2001 Oct 30.

PMID:
11736038
19.
20.

Universal learning curves of support vector machines.

Opper M, Urbanczik R.

Phys Rev Lett. 2001 May 7;86(19):4410-3.

PMID:
11328187
21.

Retarded learning: rigorous results from statistical mechanics.

Herschkowitz D, Opper M.

Phys Rev Lett. 2001 Mar 5;86(10):2174-7.

PMID:
11289883
22.

Gaussian processes for classification: mean-field algorithms.

Opper M, Winther O.

Neural Comput. 2000 Nov;12(11):2655-84.

PMID:
11110131
23.

On-line versus Off-line Learning from Random Examples: General Results.

Opper M.

Phys Rev Lett. 1996 Nov 25;77(22):4671-4674. No abstract available.

PMID:
10062597
24.

Mean field approach to Bayes learning in feed-forward neural networks.

Opper M, Winther O.

Phys Rev Lett. 1996 Mar 11;76(11):1964-1967. No abstract available.

PMID:
10060565
25.

Bounds for predictive errors in the statistical mechanics of supervised learning.

Opper M, Haussler D.

Phys Rev Lett. 1995 Nov 13;75(20):3772-3775. No abstract available.

PMID:
10059723
26.

Statistical physics estimates for the complexity of feedforward neural networks.

Opper M.

Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics. 1995 Apr;51(4):3613-3618. No abstract available.

PMID:
9963043
27.

Mean-field Monte Carlo approach to the Sherrington-Kirkpatrick model with asymmetric couplings.

Eissfeller H, Opper M.

Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics. 1994 Aug;50(2):709-720. No abstract available.

PMID:
9962029
28.

Biochemical properties of recombinant human beta-glucuronidase synthesized in baby hamster kidney cells.

Gehrmann MC, Opper M, Sedlacek HH, Bosslet K, Czech J.

Biochem J. 1994 Aug 1;301 ( Pt 3):821-8.

29.

Learning and generalization in a two-layer neural network: The role of the Vapnik-Chervonvenkis dimension.

Opper M.

Phys Rev Lett. 1994 Mar 28;72(13):2113-2116. No abstract available.

PMID:
10055791
30.

Generalization ability of perceptrons with continuous outputs.

Bös S, Kinzel W, Opper M.

Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics. 1993 Feb;47(2):1384-1391. No abstract available.

PMID:
9960140
31.

Generalization in a two-layer neural network.

Schwarze H, Opper M, Kinzel W.

Phys Rev A. 1992 Nov 15;46(10):R6185-R6188. No abstract available.

PMID:
9908009
32.

Phase transition and 1/f noise in a game dynamical model.

Opper M, Diederich S.

Phys Rev Lett. 1992 Sep 7;69(10):1616-1619. No abstract available.

PMID:
10046267
33.

New method for studying the dynamics of disordered spin systems without finite-size effects.

Eissfeller H, Opper M.

Phys Rev Lett. 1992 Mar 30;68(13):2094-2097. No abstract available.

PMID:
10045302
34.

Tilinglike learning in the parity machine.

Biehl M, Opper M.

Phys Rev A. 1991 Nov 15;44(10):6888-6894. No abstract available.

PMID:
9905815
35.

Generalization performance of Bayes optimal classification algorithm for learning a perceptron.

Opper M, Haussler D.

Phys Rev Lett. 1991 May 20;66(20):2677-2680. No abstract available.

PMID:
10043583
36.

Replicators with random interactions: A solvable model.

Diederich S, Opper M.

Phys Rev A Gen Phys. 1989 Apr 15;39(8):4333-4336. No abstract available.

PMID:
9901778
37.

Molecular basis for the regulation of cell fate by the lethal (2) giant larvae tumour suppressor gene of Drosophila melanogaster.

Mechler BM, Török I, Schmidt M, Opper M, Kuhn A, Merz R, Protin U.

Ciba Found Symp. 1989;142:166-78; discussion 178-80.

PMID:
2545420
38.

Learning times of neural networks: Exact solution for a PERCEPTRON algorithm.

Opper M.

Phys Rev A Gen Phys. 1988 Oct 1;38(7):3824-3826. No abstract available.

PMID:
9900833
39.

Structure of the l(2)gl gene of Drosophila and delimitation of its tumor suppressor domain.

Jacob L, Opper M, Metzroth B, Phannavong B, Mechler BM.

Cell. 1987 Jul 17;50(2):215-25.

PMID:
3036370
40.
41.

Learning of correlated patterns in spin-glass networks by local learning rules.

Diederich S, Opper M.

Phys Rev Lett. 1987 Mar 2;58(9):949-952. No abstract available.

PMID:
10035080

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