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Dr. Matthias Seeger

Raum Nummer: 208

 

Bild von Seeger, Matthias, Dr.

Matthias Seeger

Position: Wissenschaftler  Abteilung: 

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Artikel (7):

Seeger M und Nickisch H (März-2011) Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models SIAM Journal on Imaging Sciences 4(1) 166-199.
Seeger M, Nickisch H, Pohmann R und Schölkopf B (Januar-2010) Optimization of k-Space Trajectories for Compressed Sensing by Bayesian Experimental Design Magnetic Resonance in Medicine 63(1) 116-126.
Nguyen-Tuong D, Seeger M und Peters J (November-2009) Model Learning with Local Gaussian Process Regression Advanced Robotics 23(15) 2015-2034.
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Seeger M (Juni-2008) Cross-validation Optimization for Large Scale Structured Classification Kernel Methods Journal of Machine Learning Research 9 1147-1178.
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Seeger MW, Kakade SM und Foster DP (Mai-2008) Information Consistency of Nonparametric Gaussian Process Methods IEEE Transactions on Information Theory 54(5) 2376-2382.
Seeger MW (April-2008) Bayesian Inference and Optimal Design for the Sparse Linear Model Journal of Machine Learning Research 9 759-813.
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Steinke F, Seeger M und Tsuda K (November-2007) Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models BMC Systems Biology 1(51) 1-15.
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Beiträge zu Tagungsbänden (15):

Seeger M und Nickisch H (April-2011) Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference, 14th International Conference on Artificial Intelligence and Statistics (AISTATS 2011), International Machine Learning Society, Madison, WI, USA, 652-660, Series: JMLR Workshop and Conference Proceedings ; 15.
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Seeger MW (September-2009) Sparse linear models: Variational approximate inference and Bayesian experimental design, International Workshop on Statistical-Mechanical Informatics (IW-SMI 2009), Institute of Physics, Bristol, UK, Journal of Physics: Conference Series, 197(1), 1-13.
Seeger MW, Nickisch H, Pohmann R und Schölkopf B (Juni-2009) Bayesian Experimental Design of Magnetic Resonance Imaging Sequences In: Advances in neural information processing systems 21, , Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008), Curran, Red Hook, NY, USA, 1441-1448.
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Nickisch H und Seeger MW (Juni-2009) Convex variational Bayesian inference for large scale generalized linear models, 26th International Conference on Machine Learning (ICML 2009), ACM Press, New York, NY, USA, 761-768.
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Nguyen-Tuong D, Seeger M und Peters J (Juni-2009) Local Gaussian Process Regression for Real Time Online Model Learning and Control In: Advances in neural information processing systems 21, , Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008), Curran, Red Hook, NY, USA, 1193-1200.
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Seeger M, Sra S und Cunningham JP (Juni-2009) Workshop summary: Numerical mathematics in machine learning, 26th Annual International Conference on Machine Learning (ICML 2009), ACM Press, New York, NY, USA, 169.
Gerwinn S, Macke J, Seeger M und Bethge M (September-2008) Bayesian Inference for Spiking Neuron Models with a Sparsity Prior In: Advances in neural information processing systems 20, , Twenty-First Annual Conference on Neural Information Processing Systems (NIPS 2007), Curran, Red Hook, NY, USA, 529-536.
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Seeger MW und Nickisch H (Juli-2008) Compressed Sensing and Bayesian Experimental Design, 25th International Conference on Machine Learning (ICML 2008), ACM Press, New York, NY, USA, 912-919.
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Nguyen-Tuong D, Seeger M und Peters J (Juni-2008) Computed Torque Control with Nonparametric Regression Models, American Control Conference (ACC 2008), IEEE Service Center, Piscataway, NJ, USA, 212-217.
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Nguyen-Tuong D, Peters J, Seeger M und Schölkopf B (April-2008) Learning Inverse Dynamics: A Comparison In: Advances in computational intelligence and learning, , 16th European Symposium on Artificial Neural Networks (ESANN 2008), D-Side, Evere, Belgium, 13-18.
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Seeger M, Gerwinn S und Bethge M (September-2007) Bayesian Inference for Sparse Generalized Linear Models In: Machine Learning: ECML 2007, , 18th European Conference on Machine Learning, Springer, Berlin, Germany, 298-309, Series: Lecture Notes in Computer Science ; 4701.
Seeger M (September-2007) Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods In: Advances in Neural Information Processing Systems 19, , Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006), MIT Press, Cambridge, MA, USA, 1233-1240.
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Seeger M, Steinke F und Tsuda K (März-2007) Bayesian Inference and Optimal Design in the Sparse Linear Model, 11th International Conference on Artificial Intelligence and Statistics (AISTATS 2007), International Machine Learning Society, Madison, WI, USA, 444-451, Series: JMLR Workshop and Conference Proceedings ; 2.
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Shen Y, Ng AY und Seeger M (Mai-2006) Fast Gaussian Process Regression using KD-Trees In: Advances in neural information processing systems 18, , Nineteenth Annual Conference on Neural Information Processing Systems (NIPS 2005), MIT Press, Cambridge, MA, USA, 1225-1232.
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Kakade S, Seeger M und Foster D (Mai-2006) Worst-Case Bounds for Gaussian Process Models In: Advances in neural information processing systems 18, , Nineteenth Annual Conference on Neural Information Processing Systems (NIPS 2005), MIT Press, Cambridge, MA, USA, 619-626.
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Beiträge zu Büchern (1):

Nguyen-Tuong D, Seeger M und Peters J: Real-Time Local GP Model Learning, 193-207. In: From Motor Learning to Interaction Learning in Robots, (Ed) O. Sigaud, Springer, Berlin, Germany, (Januar-2010).
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Technische Berichte (4):

Seeger M und Nickisch H: Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, (Dezember-2010).
Seeger M und Nickisch H: Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, (August-2010).
Seeger MW und Nickisch H: Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models, 175, Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, (September-2008).
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Seeger M und Chapelle O: Cross-Validation Optimization for Structured Hessian Kernel Methods, Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, (Februar-2006).
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Poster (4):

Blecher W, Pohmann R, Schölkopf B und Seeger M (Oktober-2011): Model based reconstruction for GRE EPI, 28th Annual Scientific Meeting ESMRMB 2011, Leipzig, Germany, Magnetic Resonance Materials in Physics, Biology and Medicine, 24(Supplement 1) 493-494.
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Seeger M, Nickisch H, Pohmann R und Schölkopf B (April-21-2009): 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.
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Gerwinn S, Seeger M, Zeck G und Bethge M (April-2007): Bayesian Neural System identification: error bars, receptive fields and neural couplings, 7th Meeting of the German Neuroscience Society, 31st Göttingen Neurobiology Conference, Göttingen, Germany, Neuroforum, 13(Supplement) 360.
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Gerwinn S, Seeger M, Zeck G und Bethge M (Februar-2007): Bayesian Receptive Fields and Neural Couplings with Sparsity Prior and Error Bars, Computational and Systems Neuroscience Meeting (COSYNE 2007), Salt Lake City, UT, USA.

Vorträge (5):

Seeger M, Nickisch H, Pohmann R und Schölkopf B (November-20-2008) Invited Lecture: Bayesian Optimization of Magnetic Resonance Imaging Sequences, Workshop Machine Learning Approaches to Representational Learning and Recognition in Vision, Frankfurt a.M., Germany.
Seeger M (Juli-2008) Invited Lecture: Sparse Linear Models: Bayesian Inference and Experimental Design, 25th International Conference on Machine Learning (ICML 2008), Helsinki, Finland.
Seeger M (Februar-20-2008) Invited Lecture: Expectation Propagation, Experimental Design for the Sparse Linear Model, University of Cambridge: Engineering Department, Cambridge, UK.
Gerwinn S, Seeger M, Zeck G und Bethge M (November-2006) Abstract Talk: Bayesian Neural System identification: error bars, receptive fields and neural couplings, 7th Conference of the Junior Neuroscientists of Tübingen (NeNa 2006), Oberjoch, Germany 9.
Steinke F, Seeger M und Tsuda K (Juli-26-2006) Invited Lecture: 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.

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Last updated: Montag, 22.05.2017