Suchergebnisse

Konferenzbeitrag (12)

21.
Konferenzbeitrag
Seeger, M.; Steinke, F.; Tsuda, K.: Bayesian Inference and Optimal Design in the Sparse Linear Model. In: Artificial Intelligence and Statistics, 21-24 March 2007, San Juan, Puerto Rico, S. 444 - 451 (Hg. Meila, M.; Shen, X.). 11th International Conference on Artificial Intelligence and Statistics (AISTATS 2007), San Juan, Puerto Rico, 21. März 2007 - 24. März 2007. International Machine Learning Society, Madison, WI, USA (2007)

Meeting Abstract (1)

22.
Meeting Abstract
Gerwinn, S.; Seeger, M.; Zeck, G.; Bethge, M.: Bayesian Neural System identification: error bars, receptive fields and neural couplings. In 7th Conference of Tuebingen Junior Neuroscientists (NeNa 2006), S. 9. 7th Conference of Tuebingen Junior Neuroscientists (NeNa 2006), Oberjoch, Germany, 26. November 2006 - 28. November 2006. (2006)

Vortrag (4)

23.
Vortrag
Seeger, M.; Nickisch, H.; Pohmann, R.; Schölkopf, B.: Bayesian Experimental Design of MRI Sequences. Workshop on Machine Learning: Approaches to Representational Learning and Recognition in Vision, Frankfurt a.M., Germany (2008)
24.
Vortrag
Seeger, M.: Sparse Linear Models: Bayesian Inference and Experimental Design. 25th International Conference on Machine Learning (ICML 2008), Helsinki, Finland (2008)
25.
Vortrag
Seeger, M.: Expectation Propagation, Experimental Design for the Sparse Linear Model. University of Cambridge: Engineering Department, Cambridge, UK (2008)
26.
Vortrag
Steinke, F.; Seeger, M.; Tsuda, K.: Experimental Design for Efficient Identification of Gene Regulatory Networks using Sparse Bayesian Models. International Workshop on Probabilistic Modelling in Computational Biology (PMCB 2007), Wien, Austria (2006)

Poster (4)

27.
Poster
Blecher, W.; Pohmann, R.; Schölkopf, B.; Seeger, M.: Model based reconstruction for GRE EPI. 28th Annual Scientific Meeting ESMRMB 2011, Leipzig, Germany (2011)
28.
Poster
Seeger, M.; Nickisch, H.; Pohmann, R.; Schölkopf, B.: Optimization of k-Space Trajectories by Bayesian Experimental Design. 17th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM 2009), Honolulu, HI, USA (2009)
29.
Poster
Gerwinn, S.; Seeger, M.; Zeck, G.; Bethge, M.: Bayesian Neural System identification: error bars, receptive fields and neural couplings. 7th Meeting of the German Neuroscience Society, 31st Göttingen Neurobiology Conference, Göttingen, Germany (2007)
30.
Poster
Gerwinn, S.; Seeger, M.; Zeck, G.; Bethge, M.: Bayesian Receptive Fields and Neural Couplings with Sparsity Prior and Error Bars. Computational and Systems Neuroscience Meeting (COSYNE 2007), Salt Lake City, UT, USA (2007)

Bericht (5)

31.
Bericht
Nickisch, H.; Seeger, M.: Multiple Kernel Learning: A Unifying Probabilistic Viewpoint. Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2011), 12 S.
32.
Bericht
Seeger, M.; Nickisch, H.: Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference. Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2010), 16 S.
33.
Bericht
Seeger, M.; Nickisch, H.: Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models. Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2010), 11 S.
34.
Bericht
Seeger, M.; Nickisch, H.: Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models (Technical Report of the Max Planck Institute for Biological Cybernetics, 175). Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2008), 34 S.
35.
Bericht
Seeger, M.: Cross-Validation Optimization for Structured Hessian Kernel Methods. Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2006), 36 S.
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