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The success of machine learning has been demonstrated time and time again in classification, generative modelling, and reinforcement learning. In particular, we have recently seen interesting developments where ML has been applied to the natural sciences (chemistry, physics, materials science, neuroscience and biology). Here, often the data is not abundant and very costly. This workshop will focus on the unique challenges of applying machine learning to molecules and materials.
Accurate prediction of chemical and physical properties is a crucial ingredient toward rational compound design in chemical and pharmaceutical industries. Many discoveries in chemistry can be guided by screening large databases of computational molecular structures and properties, but high level quantum-chemical calculations can take up to several days per molecule or material at the required accuracy, placing the ultimate achievement of in silico design out of reach for the foreseeable future. In large part the current state of the art for such problems is the expertise of individual researchers or at best highly-specific rule-based heuristic systems. Efficient methods in machine learning, applied to property and structure prediction, can therefore have pivotal impact in enabling chemical discovery and foster fundamental insights.
Because of this, in the past few years there has been a flurry of recent work towards designing machine learning techniques for molecule [1, 2, 4-11, 13-18, 20, 21, 23-32, 34-38] and material data [1-3, 5, 6, 12, 19, 24, 33]. These works have drawn inspiration from and made significant contributions to areas of machine learning as diverse as learning on graphs to models in natural language processing. Recent advances enabled the acceleration of molecular dynamics simulations, contributed to a better understanding of interactions within quantum many-body systems and increased the efficiency of density functional theory based quantum mechanical modeling methods. This young field offers unique opportunities for machine learning researchers and practitioners, as it presents a wide spectrum of challenges and open questions, including but not limited to representations of physical systems, physically constrained models, manifold learning, interpretability, model bias, and causality.
The goal of this workshop is to bring together researchers and industrial practitioners in the fields of computer science, chemistry, physics, materials science, and biology all working to innovate and apply machine learning to tackle the challenges involving molecules and materials. In a highly interactive format, we will outline the current frontiers and present emerging research directions. We aim to use this workshop as an opportunity to establish a common language between all communities, to actively discuss new research problems, and also to collect datasets by which novel machine learning models can be benchmarked. The program is a collection of invited talks, alongside contributed posters. A panel discussion will provide different perspectives and experiences of influential researchers from both fields and also engage open participant conversation. An expected outcome of this workshop is the interdisciplinary exchange of ideas and initiation of collaboration.
References
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[2] Behler, J., Parrinello, M. (2007). Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett., 98(14), 146401.
[3] Kang, B., Ceder, G. (2009). Battery materials for ultrafast charging and discharging. Nature, 458(7235), 190.
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[5] Behler, J. (2011). Atom-centered symmetry functions for constructing high-dimensional neural network potentials. J. Chem. Phys, 134(7), 074106.
[6] Behler, J. (2011). Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations. Phys. Chem. Chem. Phys., 13(40), 17930-17955.
[7] Rupp, M., Tkatchenko, A., Müller, K.-R., von Lilienfeld, O. A. (2012). Fast and accurate modeling of molecular atomization energies with machine learning. Phys. Rev. Lett., 108(5), 058301.
[8] Snyder, J. C., Rupp, M., Hansen, K., Müller, K.-R., Burke, K. (2012). Finding density functionals with machine learning. Phys. Rev. Lett., 108(25), 253002.
[9] Montavon, G., Rupp, M., Gobre, V., Vazquez-Mayagoitia, A., Hansen, K., Tkatchenko, A., Müller, K.-R., von Lilienfeld, O. A. (2013). Machine learning of molecular electronic properties in chemical compound space. New J. Phys., 15(9), 095003.
[10] Hansen, K., Montavon, G., Biegler, F., Fazli, S., Rupp, M., Scheffler, M., Tkatchenko, A., Müller, K.-R. (2013). Assessment and validation of machine learning methods for predicting molecular atomization energies. J. Chem. Theory Comput., 9(8), 3404-3419.
[11] Bartók, A. P., Kondor, R., Csányi, G. (2013). On representing chemical environments. Phys. Rev. B, 87(18), 184115.
[12] Schütt K. T., Glawe, H., Brockherde F., Sanna A., Müller K.-R., Gross E. K. U. (2014). How to represent crystal structures for machine learning: towards fast prediction of electronic properties. Phys. Rev. B., 89(20), 205118.
[13] Ramsundar, B., Kearnes, S., Riley, P., Webster, D., Konerding, D., Pande, V. (2015). Massively multitask networks for drug discovery. arXiv preprint arXiv:1502.02072.
[14] Rupp, M., Ramakrishnan, R., & von Lilienfeld, O. A. (2015). Machine learning for quantum mechanical properties of atoms in molecules. J. Phys. Chem. Lett., 6(16), 3309-3313.
[15] V. Botu, R. Ramprasad (2015). Learning scheme to predict atomic forces and accelerate materials simulations., Phys. Rev. B, 92(9), 094306.
[16] Hansen, K., Biegler, F., Ramakrishnan, R., Pronobis, W., von Lilienfeld, O. A., Müller, K.-R., Tkatchenko, A. (2015). Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space. J. Phys. Chem. Lett, 6(12), 2326-2331.
[17] Alipanahi, B., Delong, A., Weirauch, M. T., Frey, B. J. (2015). Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nat. Biotechnol., 33(8), 831-838.
[18] Duvenaud, D. K., Maclaurin, D., Aguilera-Iparraguirre, J., Gomez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., Adams, R. P. (2015). Convolutional networks on graphs for learning molecular fingerprints. NIPS, 2224-2232.
[19] Faber F. A., Lindmaa A., von Lilienfeld, O. A., Armiento, R. (2016). Machine learning energies of 2 million elpasolite (A B C 2 D 6) crystals. Phys. Rev. Lett., 117(13), 135502.
[20] Gomez-Bombarelli, R., Duvenaud, D., Hernandez-Lobato, J. M., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., Aspuru-Guzik, A. (2016). Automatic chemical design using a data-driven continuous representation of molecules. arXiv preprint arXiv:1610.02415.
[21] Wei, J. N., Duvenaud, D, Aspuru-Guzik, A. (2016). Neural networks for the prediction of organic chemistry reactions. ACS Cent. Sci., 2(10), 725-732.
[22] Sadowski, P., Fooshee, D., Subrahmanya, N., Baldi, P. (2016). Synergies between quantum mechanics and machine learning in reaction prediction. J. Chem. Inf. Model., 56(11), 2125-2128.
[23] Lee, A. A., Brenner, M. P., Colwell L. J. (2016). Predicting protein-ligand affinity with a random matrix framework. Proc. Natl. Acad. Sci., 113(48), 13564-13569.
[24] Behler, J. (2016). Perspective: Machine learning potentials for atomistic simulations. J. Chem. Phys., 145(17), 170901.
[25] De, S., Bartók, A. P., Csányi, G., Ceriotti, M. (2016). Comparing molecules and solids across structural and alchemical space. Phys. Chem. Chem. Phys., 18(20), 13754-13769.
[26] Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K.-R., Tkatchenko, A. (2017). Quantum-chemical insights from deep tensor neural networks. Nat. Commun., 8, 13890.
[27] Segler, M. H., Waller, M. P. (2017). Neural‐symbolic machine learning for retrosynthesis and reaction prediction. Chem. Eur. J., 23(25), 5966-5971.
[28] Kusner, M. J., Paige, B., Hernández-Lobato, J. M. (2017). Grammar variational autoencoder. arXiv preprint arXiv:1703.01925.
[29] Coley, C. W., Barzilay, R., Jaakkola, T. S., Green, W. H., Jensen K. F. (2017). Prediction of organic reaction outcomes using machine learning. ACS Cent. Sci., 3(5), 434-443.
[30] Altae-Tran, H., Ramsundar, B., Pappu, A. S., Pande, V. (2017). Low data drug discovery with one-shot learning. ACS Cent. Sci., 3(4), 283-293.
[31] Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., Dahl, G. E. (2017). Neural message passing for quantum chemistry. arXiv preprint arXiv:1704.01212.
[32] Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, Igor, Schütt, K. T., Müller, K.-R. (2017). Machine learning of accurate energy-conserving molecular force fields. Sci. Adv., 3(5), e1603015.
[33] Ju, S., Shiga T., Feng L., Hou Z., Tsuda, K., Shiomi J. (2017). Designing nanostructures for phonon transport via bayesian optimization. Phys. Rev. X, 7(2), 021024.
[34] Ramakrishnan, R, von Lilienfeld, A. (2017). Machine learning, quantum chemistry, and chemical space. Reviews in Computational Chemistry, 225-256.
[35] Hernandez-Lobato, J. M., Requeima, J., Pyzer-Knapp, E. O., Aspuru-Guzik, A. (2017). Parallel and distributed Thompson sampling for large-scale accelerated exploration of chemical space. arXiv preprint arXiv:1706.01825.
[36] Smith, J., Isayev, O., Roitberg, A. E. (2017). ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chem. Sci., 8(4), 3192-3203.
[37] Brockherde, F., Li, L., Burke, K., Müller, K.-R. By-passing the Kohn-Sham equations with machine learning. Nat. Commun., in press.
[38] Schütt, K. T., Kindermans, P. J., Sauceda, H. E., Chmiela, S., Tkatchenko, A., Müller, K. R. (2017). MolecuLeNet: A continuous-filter convolutional neural network for modeling quantum interactions. NIPS (accepted).
Fri 8:00 a.m. - 8:20 a.m.
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Opening remarks
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Klaus-Robert Müller 🔗 |
Fri 8:20 a.m. - 8:45 a.m.
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Machine Learning for Molecular Materials Design
(
Invited talk
)
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Alan Aspuru-Guzik 🔗 |
Fri 8:45 a.m. - 9:00 a.m.
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TBA4
(
Invited talk
)
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Robert DiStasio Jr. 🔗 |
Fri 9:00 a.m. - 9:25 a.m.
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New Density Functionals Created by Machine Learning
(
Invited talk
)
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kieron burke 🔗 |
Fri 9:35 a.m. - 10:15 a.m.
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Poster spotlights
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Emma Strubell · Garrett Goh · Masashi Tsubaki · Théophile Gaudin · Philippe Schwaller · Matthew Ragoza · Rafael Gomez-Bombarelli 🔗 |
Fri 10:45 a.m. - 11:05 a.m.
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Quantum Machine Learning
(
Invited talk
)
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Anatole von Lilienfeld 🔗 |
Fri 11:05 a.m. - 11:25 a.m.
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Machine Learning in Organic Synthesis Planning And Execution
(
Invited talk
)
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Klavs Jensen 🔗 |
Fri 11:25 a.m. - 11:40 a.m.
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Neural-network Quantum States
(
Invited talk
)
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Giuseppe Carleo 🔗 |
Fri 11:40 a.m. - 11:55 a.m.
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Quantitative Attribution: Do Neural Network Models Learn the Correct Chemistry?
(
Invited talk
)
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Lucy Colwell 🔗 |
Fri 1:55 p.m. - 2:20 p.m.
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TBA11
(
Invited talk
)
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Alexander Smola 🔗 |
Fri 2:00 p.m. - 2:15 p.m.
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ChemTS: An Efficient Python Library for De Novo Molecular Generation
(
Invited talk
)
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Koji Tsuda 🔗 |
Fri 2:15 p.m. - 2:35 p.m.
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Symmetry Matters: Learning Scalars and Tensors in Materials and Molecules
(
Invited talk
)
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Michele Ceriotti 🔗 |
Fri 2:35 p.m. - 2:45 p.m.
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Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields
(
Short
)
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Stefan Chmiela 🔗 |
Fri 3:25 p.m. - 3:45 p.m.
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N-body Neural Networks: A General Compositional Architecture For Representing Multiscale Physical Systems
(
Invited talk
)
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Risi Kondor 🔗 |
Fri 3:45 p.m. - 4:05 p.m.
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Distilling Expensive Simulations with Neural Networks
(
Invited talk
)
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Oriol Vinyals 🔗 |
Fri 4:05 p.m. - 4:20 p.m.
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Automatic Chemical Design Using a Data-driven Continuous Representation of Molecules
(
Invited talk
)
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David Duvenaud 🔗 |
Fri 4:20 p.m. - 4:40 p.m.
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Planning Chemical Syntheses with Neural Networks and Monte Carlo Tree Search
(
Invited talk
)
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Marwin Segler 🔗 |
Fri 5:50 p.m. - 6:05 p.m.
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Closing remarks
|
José Miguel Hernández-Lobato 🔗 |
Author Information
Kristof Schütt (TU Berlin)
Klaus-Robert Müller (TU Berlin)
Anatole von Lilienfeld (Universität Basel)
José Miguel Hernández-Lobato (University of Cambridge)
Klaus-Robert Müller (TU Berlin)
Alan Aspuru-Guzik (Harvard University)
Bharath Ramsundar (Stanford)
Matt Kusner (University of Oxford)
Brooks Paige (Alan Turing Institute / University of Cambridge)
Stefan Chmiela (Technische Universität Berlin)
Alexandre Tkatchenko (University of Luxembourg)
Anatole von Lilienfeld (University of Basel)
Koji Tsuda (The University of Tokyo / RIKEN)
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Grégoire Montavon · Klaus-Robert Müller · Marco Cuturi -
2015 Workshop: Black box learning and inference »
Josh Tenenbaum · Jan-Willem van de Meent · Tejas Kulkarni · S. M. Ali Eslami · Brooks Paige · Frank Wood · Zoubin Ghahramani -
2015 Poster: Stochastic Expectation Propagation »
Yingzhen Li · José Miguel Hernández-Lobato · Richard Turner -
2015 Spotlight: Stochastic Expectation Propagation »
Yingzhen Li · José Miguel Hernández-Lobato · Richard Turner -
2014 Poster: Asynchronous Anytime Sequential Monte Carlo »
Brooks Paige · Frank Wood · Arnaud Doucet · Yee Whye Teh -
2014 Oral: Asynchronous Anytime Sequential Monte Carlo »
Brooks Paige · Frank Wood · Arnaud Doucet · Yee Whye Teh -
2014 Poster: Predictive Entropy Search for Efficient Global Optimization of Black-box Functions »
José Miguel Hernández-Lobato · Matthew Hoffman · Zoubin Ghahramani -
2014 Poster: Covariance shrinkage for autocorrelated data »
Daniel Bartz · Klaus-Robert Müller -
2014 Poster: Gaussian Process Volatility Model »
Yue Wu · José Miguel Hernández-Lobato · Zoubin Ghahramani -
2014 Spotlight: Predictive Entropy Search for Efficient Global Optimization of Black-box Functions »
José Miguel Hernández-Lobato · Matthew Hoffman · Zoubin Ghahramani -
2013 Poster: Learning Feature Selection Dependencies in Multi-task Learning »
Daniel Hernández-lobato · José Miguel Hernández-Lobato -
2013 Poster: Robust Spatial Filtering with Beta Divergence »
Wojciech Samek · Duncan Blythe · Klaus-Robert Müller · Motoaki Kawanabe -
2013 Poster: Generalizing Analytic Shrinkage for Arbitrary Covariance Structures »
Daniel Bartz · Klaus-Robert Müller -
2013 Spotlight: Robust Spatial Filtering with Beta Divergence »
Wojciech Samek · Duncan Blythe · Klaus-Robert Müller · Motoaki Kawanabe -
2013 Spotlight: Generalizing Analytic Shrinkage for Arbitrary Covariance Structures »
Daniel Bartz · Klaus-Robert Müller -
2013 Poster: Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits »
Ben Shababo · Brooks Paige · Ari Pakman · Liam Paninski -
2013 Poster: Gaussian Process Conditional Copulas with Applications to Financial Time Series »
José Miguel Hernández-Lobato · James R Lloyd · Daniel Hernández-lobato -
2013 Spotlight: Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits »
Ben Shababo · Brooks Paige · Ari Pakman · Liam Paninski -
2012 Poster: Collaborative Gaussian Processes for Preference Learning »
Neil Houlsby · José Miguel Hernández-Lobato · Ferenc Huszar · Zoubin Ghahramani -
2012 Poster: Learning Invariant Representations of Molecules for Atomization Energy Prediction »
Grégoire Montavon · Katja Hansen · Siamac Fazli · Matthias Rupp · Franziska Biegler · Andreas Ziehe · Alexandre Tkatchenko · Anatole von Lilienfeld · Klaus-Robert Müller -
2012 Poster: Semi-Supervised Domain Adaptation with Non-Parametric Copulas »
David Lopez-Paz · José Miguel Hernández-Lobato · Bernhard Schölkopf -
2012 Spotlight: Semi-Supervised Domain Adaptation with Non-Parametric Copulas »
David Lopez-Paz · José Miguel Hernández-Lobato · Bernhard Schölkopf -
2011 Demonstration: Real-time social media analysis with TWIMPACT »
Mikio L Braun · Matthias L Jugel · Klaus-Robert Müller -
2011 Poster: Robust Multi-Class Gaussian Process Classification »
Daniel Hernández-lobato · José Miguel Hernández-Lobato · Pierre Dupont -
2010 Workshop: Charting Chemical Space: Challenges and Opportunities for AI and Machine Learning »
Pierre Baldi · Klaus-Robert Müller · Gisbert Schneider -
2010 Poster: Layer-wise analysis of deep networks with Gaussian kernels »
Grégoire Montavon · Mikio L Braun · Klaus-Robert Müller -
2009 Poster: Efficient and Accurate Lp-Norm Multiple Kernel Learning »
Marius Kloft · Ulf Brefeld · Soeren Sonnenburg · Pavel Laskov · Klaus-Robert Müller · Alexander Zien -
2009 Poster: Subject independent EEG-based BCI decoding »
Siamac Fazli · Cristian Grozea · Márton Danóczy · Benjamin Blankertz · Florin Popescu · Klaus-Robert Müller -
2009 Spotlight: Subject independent EEG-based BCI decoding »
Siamac Fazli · Cristian Grozea · Márton Danóczy · Benjamin Blankertz · Florin Popescu · Klaus-Robert Müller -
2008 Poster: Playing Pinball with non-invasive BCI »
Michael W Tangermann (ne Schröder) · Matthias Krauledat · Konrad Grzeska · Max Sagebaum · Benjamin Blankertz · Klaus-Robert Müller -
2008 Poster: Estimating vector fields using sparse basis field expansions »
Stefan Haufe · Vadim Nikulin · Andreas Ziehe · Klaus-Robert Müller · Guido Nolte -
2007 Spotlight: Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing »
Benjamin Blankertz · Motoaki Kawanabe · Ryota Tomioka · Friederike Hohlefeld · Vadim Nikulin · Klaus-Robert Müller -
2007 Poster: Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing »
Benjamin Blankertz · Motoaki Kawanabe · Ryota Tomioka · Friederike Hohlefeld · Vadim Nikulin · Klaus-Robert Müller -
2007 Poster: Heterogeneous Component Analysis »
Shigeyuki Oba · Motoaki Kawanabe · Klaus-Robert Müller · Shin Ishii -
2007 Poster: Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach »
José Miguel Hernández-Lobato · Tjeerd M Dijkstra · Tom Heskes -
2007 Spotlight: Heterogeneous Component Analysis »
Shigeyuki Oba · Motoaki Kawanabe · Klaus-Robert Müller · Shin Ishii -
2006 Workshop: Current Trends in Brain-Computer Interfacing »
Klaus-Robert Müller · José del R. Millán · Matthias Krauledat · Roderick Murray-Smith · Benjamin Blankertz -
2006 Poster: Logistic Regression for Single Trial EEG Classification »
Ryota Tomioka · Kazuyuki Aihara · Klaus-Robert Müller -
2006 Poster: Towards Zero-Training for Brain-Computer Interface Experiments »
Matthias Krauledat · Michael Schröder · Benjamin Blankertz · Klaus-Robert Müller -
2006 Spotlight: Logistic Regression for Single Trial EEG Classification »
Ryota Tomioka · Kazuyuki Aihara · Klaus-Robert Müller -
2006 Poster: Inducing Metric Violations in Human Similarity Judgements »
Julian Laub · Jakob H Macke · Klaus-Robert Müller · Felix A Wichmann -
2006 Poster: Denoising and Dimension Reduction in Feature Space »
Mikio L Braun · Joachim M Buhmann · Klaus-Robert Müller