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E

Learning in Neural Networks: Solvable Dynamics

Opper, M.

Europhys. Lett., 389–392. 1989

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C

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

Gruenhage, G., Opper, M. and Barthelme, S.

Computational Statistics & Data Analysis. Elsevier, 51 - 65. 2016

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Tractable Inference for Probabilistic Data Models

Csató, L., Opper, M. and Winther, O.

Complexity, 64-68. 2003

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B

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

Opper, M., Dewar, M. A., Kadirkamanathan, V. and Sanguinetti, G.

BMC Systems Biology. BMC Systems Biology %, 669 - 676. 2010

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Switching Regulatory Models of Cellular Stress Response

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

Bioinformatics, 1280-1286. 2009

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Learning combinatorial transcriptional dynamics from gene expression data

Opper, M. and Sanguinetti, G.

Bioinformatics. Oxford Journals, 1623-1629. 2010

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Switching Regulatory Models of Cellular Stress Response

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

Bioinformatics 2009

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A

2

Dynamical Functional Theory for Compressed Sensing

Çakmak, B., Opper, M., Winther, O. and Fleury, B. H.

2017 IEEE International Symposium on Information Theory (ISIT). IEEE Press, 2143-2147. 2017

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