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• Aberdeen, Douglas • Büche, Dirk • Barral, Yves • Belew, Richard K. • Borgwardt, Karsten • Bray, Matthieu • Cannarozzi, Gina • Chen, Jing • Chik, Desmond • Chong, Adrian • Dayan, Peter • Eldracher, Martin • Faty (joint first authors), Mahamadou • Friberg, Markus T. • Günter, Simon • Gers, Felix A. • Giannakopoulos, Xavier • Gonnet, Gaston H. • Gonnet, Pedro • Graepel, Thore • Grefenstette, John J. • Hansen, Nikolaus • Kamenetsky, Dmitry • Karatzoglou, Alexandros • Klapper-Rybicka, Magdalena • Koller-Meier, Esther • Kondor, Risi • Koumoutsakos, Petros • Kriegel, Hans-Peter • Li, Zhidong • Müller, Pascal • Müller, Sybille • McInerney, John • Murphy, Kevin • Rohr, Peter von • Roth, Alexander C. • Schmidhuber, Jürgen • Schmidt, Mark W. • Sejnowski, Terrence J. • Smola, Alex J. • Sunehag, Peter • Trumpf, Jochen • Van Gool, Luc • Viola, Paul A. • Vishwanathan, S. V. N. • Yu, Jin • Yu, Zhenghua • Zhao, Jieyu •
Aberdeen, Douglas
N. N. Schraudolph, J. Yu, and D. Aberdeen.
Fast Online Policy Gradient Learning with SMD Gain
Vector Adaptation. In Advances in Neural
Information Processing Systems (NIPS), pp. 1185–1192, MIT Press, Cambridge,
MA, 2006.
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Büche, Dirk
D. Büche, N. N. Schraudolph, and P.
Koumoutsakos. Accelerating Evolutionary Algorithms with
Gaussian Process Fitness Function Models. IEEE Transactions on Systems, Man,
and Cybernetics, C35(2):183–194, 2005.
Earlier
version
Details
Download:
[pdf] [djvu] [ps.gz]
D. Büche, N. N. Schraudolph, and P.
Koumoutsakos. Accelerating Evolutionary Algorithms Using
Fitness Function Models. In Genetic and Evolutionary Computation Conference
Workshop Program, pp. 166–169, AAAI, Chicago, 2003.
Latest
version
Details
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[pdf] [djvu] [ps.gz]
Barral, Yves
G. Cannarozzi, N. N. Schraudolph, M. Faty (joint first authors),
P. v. Rohr, M. T. Friberg, A. C. Roth, G. H. Gonnet, and Y. Barral.
A Role for Codon Order in Translation Dynamics. Cell,
141(2):355–367, 16 Apr 2010.
Details
Download:
[pdf] [djvu] [ps.gz]
M. T. Friberg, P. Gonnet, Y. Barral, N. N. Schraudolph,
and G. H. Gonnet. Measures of Codon Bias in Yeast, the tRNA Pairing
Index and Possible DNA Repair Mechanisms. In Algorithms in Bioinformatics:
6th Intl. Workshop (WABI), pp. 1–11, Springer
Verlag, Berlin, Zurich, Switzerland, 2006.
Details
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[pdf] [djvu] [ps.gz]
Belew, Richard K.
R. K. Belew, J. McInerney, and N. N. Schraudolph.
Evolving Networks: Using the Genetic Algorithm with
Connectionist Learning. In C. G. Langton, C. Taylor, J. D. Farmer, and S. Rasmussen,
editors, Artificial Life II, SFI Studies in the Sciences of Complexity: Proceedings,
pp. 511–547, Addison-Wesley, Redwood City, CA, 1992.
Details
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[pdf] [djvu] [ps.gz]
N. N. Schraudolph and R. K. Belew.
Dynamic Parameter Encoding for Genetic Algorithms. Machine Learning, 9:9–21,
1992.
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Borgwardt, Karsten
S. Vishwanathan, N.
N. Schraudolph, R. Kondor, and K. Borgwardt. Graph Kernels.
Journal of Machine Learning Research,
11:1201–1242, 2010.
Short version
Details
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[pdf] [djvu] [ps.gz]
K. M. Borgwardt, H. Kriegel, S.
Vishwanathan, and N. N. Schraudolph.
Graph Kernels for Disease Outcome Prediction from
Protein-Protein Interaction Networks. In Proc. Pacific Symposium on Biocomputing
(PSB), pp. 4–15, World Scientific, Maui, Hawaii, 2007.
Details
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S. Vishwanathan, K. Borgwardt,
and N. N. Schraudolph.
Fast Computation of Graph Kernels. In Advances
in Neural Information Processing Systems (NIPS), pp. 1449–1456, MIT
Press, Cambridge, MA, 2007.
Long version
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Bray, Matthieu
M. Bray, E. Koller-Meier, N. N. Schraudolph,
and L. Van Gool. Fast Stochastic Optimization for Articulated
Structure Tracking. Image and Vision Computing, 25(3):352–364, 2007.
Earlier version
Details
Download:
[pdf] [djvu] [ps.gz]
M. Bray, E. Koller-Meier, P. Müller, N.
N. Schraudolph, and L. Van Gool. Stochastic Optimization
for High-Dimensional Tracking in Dense Range Maps. IEE Proceedings
Vision, Image & Signal Processing, 152(4):501–512, 2005.
Earlier
version
Details
Download:
[pdf] [djvu] [ps.gz]
M. Bray, E. Koller-Meier, N. N. Schraudolph,
and L. Van Gool. Stochastic Meta-Descent for Tracking Articulated
Structures. In IEEE Workshop on Articulated and Nonrigid Motion,
Conference on Computer Vision and Pattern Recognition (CVPR), Washington, D.C.,
2004.
Latest version
Details
Download:
[pdf] [djvu] [ps.gz]
M. Bray, E. Koller-Meier, P. Müller, L. Van Gool, and N.
N. Schraudolph. 3D Hand Tracking by Rapid Stochastic Gradient
Descent Using a Skinning Model. In First European Conference
on Visual Media Production (CVMP), pp. 59–68, London, 2004.
Latest
version
Details
Download:
[pdf] [djvu] [ps.gz]
Cannarozzi, Gina
G. Cannarozzi, N. N. Schraudolph, M. Faty (joint first authors),
P. v. Rohr, M. T. Friberg, A. C. Roth, G. H. Gonnet, and Y. Barral.
A Role for Codon Order in Translation Dynamics. Cell,
141(2):355–367, 16 Apr 2010.
Details
Download:
[pdf] [djvu] [ps.gz]
Chen, Jing
Z. Li, J. Chen, and N. N. Schraudolph.
An Improved Mean-Shift Tracker with Kernel Prediction and Scale Optimisation
Targeting for Low-Frame-Rate Video Tracking. In 19th
Intl. Conf. Pattern Recognition (ICPR), Tampa, Florida, 2008.
Details
Download:
[pdf] [djvu] [ps.gz]
Z. Li, J. Chen, A. Chong, Z. Yu, and N. N. Schraudolph.
Using Stochastic Gradient-Descent Scheme in Appearance
Model Based Face Tracking. In Proc. Intl. Workshop Multimedia Signal Processing
(MMSP), IEEE, Cairns, Australia, 2008.
Details
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[pdf] [djvu] [ps.gz]
Chik, Desmond
D. Chik, J. Trumpf, and N. N. Schraudolph.
Using an Adaptive VAR Model for Motion Prediction
in 3D Hand Tracking. In 8th Intl. Conf. Automatic
Face & Gesture Recognition (FG), IEEE, Amsterdam, Netherlands, 2008.
Details
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D. Chik, J. Trumpf, and N. N. Schraudolph.
3D Hand Tracking in a Stochastic Approximation Setting. In 2nd
Workshop on Human Motion: Understanding, Modeling, Capture and Animation,
11th IEEE Intl. Conf. Computer Vision (ICCV), pp. 136–151,
Springer Verlag, Berlin, Rio de Janeiro, Brazil,
2007.
Details
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Chong, Adrian
Z. Li, J. Chen, A. Chong, Z. Yu, and N. N. Schraudolph.
Using Stochastic Gradient-Descent Scheme in Appearance
Model Based Face Tracking. In Proc. Intl. Workshop Multimedia Signal Processing
(MMSP), IEEE, Cairns, Australia, 2008.
Details
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[pdf] [djvu] [ps.gz]
Dayan, Peter
N. N. Schraudolph, P. Dayan, and T.
J. Sejnowski. Learning to Evaluate Go Positions
via Temporal Difference Methods. In N. Baba and L. C. Jain, editors, Computational
Intelligence in Games, Studies in Fuzziness and Soft Computing, pp. 77–98,
Springer Verlag, Berlin, 2001.
Earlier
version
Details
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N. N. Schraudolph, P. Dayan, and T.
J. Sejnowski. Temporal Difference Learning of Position Evaluation
in the Game of Go. In Advances in Neural
Information Processing Systems (NIPS), pp. 817–824, Morgan Kaufmann,
San Francisco, CA, 1994.
Latest version
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Eldracher, Martin
N. N. Schraudolph, M. Eldracher, and J.
Schmidhuber. Processing Images by Semi-Linear Predictability Minimization.
Network: Computation in Neural Systems, 10(2):133–169, 1999.
Details
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Faty (joint first authors), Mahamadou
G. Cannarozzi, N. N. Schraudolph, M. Faty (joint first authors),
P. v. Rohr, M. T. Friberg, A. C. Roth, G. H. Gonnet, and Y. Barral.
A Role for Codon Order in Translation Dynamics. Cell,
141(2):355–367, 16 Apr 2010.
Details
Download:
[pdf] [djvu] [ps.gz]
Friberg, Markus T.
G. Cannarozzi, N. N. Schraudolph, M. Faty (joint first authors),
P. v. Rohr, M. T. Friberg, A. C. Roth, G. H. Gonnet, and Y. Barral.
A Role for Codon Order in Translation Dynamics. Cell,
141(2):355–367, 16 Apr 2010.
Details
Download:
[pdf] [djvu] [ps.gz]
M. T. Friberg, P. Gonnet, Y. Barral, N. N. Schraudolph,
and G. H. Gonnet. Measures of Codon Bias in Yeast, the tRNA Pairing
Index and Possible DNA Repair Mechanisms. In Algorithms in Bioinformatics:
6th Intl. Workshop (WABI), pp. 1–11, Springer
Verlag, Berlin, Zurich, Switzerland, 2006.
Details
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[pdf] [djvu] [ps.gz]
Günter, Simon
J. Yu, S. Vishwanathan, S.
Günter, and N. N. Schraudolph.
A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in
Machine Learning. Journal of Machine Learning
Research, 11:1145–1200, 2010.
Short
version
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J. Yu, S. Vishwanathan, S.
Günter, and N. N. Schraudolph.
A Quasi-Newton Approach to Nonsmooth Convex Optimization. In Proc. 25th
Intl. Conf. Machine Learning (ICML), pp. 1216–1223, Omnipress, Helsinki,
Finland, 2008.
Long version
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S. Günter, N. N. Schraudolph, and S. Vishwanathan.
Fast Iterative Kernel Principal Component Analysis.
Journal of Machine Learning Research, 8:1893–1918, 2007.
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N. N. Schraudolph, S. Günter, and S. Vishwanathan.
Fast Iterative Kernel PCA. In Advances in
Neural Information Processing Systems (NIPS), pp. 1225–1232, MIT Press,
Cambridge, MA, 2007.
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N. N. Schraudolph, J. Yu, and S. Günter.
A Stochastic Quasi-Newton Method for Online Convex
Optimization. In Proc. 11th Intl. Conf. Artificial
Intelligence and Statistics (AIstats), pp. 436–443,
Journal of Machine Learning Research, San Juan, Puerto Rico, 2007.
Details
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Gers, Felix A.
F. A. Gers, N. N. Schraudolph, and J. Schmidhuber.
Learning Precise Timing with LSTM Recurrent Networks. Journal of Machine Learning Research, 3:115–143,
2002.
Details
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[pdf] [djvu] [ps.gz]
Giannakopoulos, Xavier
N. N. Schraudolph and X. Giannakopoulos.
Online Independent Component Analysis With Local Learning
Rate Adaptation. In Advances in Neural Information
Processing Systems (NIPS), pp. 789–795, The MIT Press, Cambridge, MA,
2000.
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Gonnet, Gaston H.
G. Cannarozzi, N. N. Schraudolph, M. Faty (joint first authors),
P. v. Rohr, M. T. Friberg, A. C. Roth, G. H. Gonnet, and Y. Barral.
A Role for Codon Order in Translation Dynamics. Cell,
141(2):355–367, 16 Apr 2010.
Details
Download:
[pdf] [djvu] [ps.gz]
M. T. Friberg, P. Gonnet, Y. Barral, N. N. Schraudolph,
and G. H. Gonnet. Measures of Codon Bias in Yeast, the tRNA Pairing
Index and Possible DNA Repair Mechanisms. In Algorithms in Bioinformatics:
6th Intl. Workshop (WABI), pp. 1–11, Springer
Verlag, Berlin, Zurich, Switzerland, 2006.
Details
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[pdf] [djvu] [ps.gz]
Gonnet, Pedro
M. T. Friberg, P. Gonnet, Y. Barral, N. N. Schraudolph,
and G. H. Gonnet. Measures of Codon Bias in Yeast, the tRNA Pairing
Index and Possible DNA Repair Mechanisms. In Algorithms in Bioinformatics:
6th Intl. Workshop (WABI), pp. 1–11, Springer
Verlag, Berlin, Zurich, Switzerland, 2006.
Details
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[pdf] [djvu] [ps.gz]
Graepel, Thore
N. N. Schraudolph and T. Graepel.
Combining Conjugate Direction Methods with Stochastic Approximation
of Gradients. In Proc. 9th Intl. Workshop Artificial
Intelligence and Statistics (AIstats), pp. 7–13, Society for Artificial
Intelligence and Statistics, Key West, Florida, 2003.
Earlier
version Related paper
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T. Graepel and N. N. Schraudolph.
Stable Adaptive Momentum for Rapid Online Learning in Nonlinear Systems.
In Proc. Intl. Conf. Artificial Neural Networks (ICANN), pp. 450–455,
Springer Verlag, Berlin, Madrid, Spain, 2002.
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N. N. Schraudolph and T. Graepel.
Towards Stochastic Conjugate Gradient Methods. In Proc. 9th Intl.
Conf. Neural Information Processing (ICONIP), pp. 853–856,
IEEE, 2002.
Related paper
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N. N. Schraudolph and T. Graepel.
Conjugate Directions for Stochastic Gradient Descent. In Proc. Intl. Conf.
Artificial Neural Networks (ICANN), pp. 1351–1356, Springer
Verlag, Berlin, Madrid, Spain, 2002.
Latest
version Related paper
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Grefenstette, John J.
N. N. Schraudolph and J. J. Grefenstette.
A User's Guide to GAucsd 1.4. Technical Report CS92-249, University
of California, San Diego, 1992.
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Hansen, Nikolaus
S. Müller, N. N. Schraudolph, P. Koumoutsakos,
and N. Hansen. Step Size Adaptation in Evolution Strategies---Two Approaches.
In Genetic and Evolutionary Computation Conference Workshop Program,
pp. 161–164, AAAI, New York, 2002.
Details
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Kamenetsky, Dmitry
N. N. Schraudolph and D. Kamenetsky.
Efficient Exact Inference in Planar Ising Models. In
Advances in Neural Information Processing Systems (NIPS), pp. 1417–1424,
Curran Associates, Inc., 2009.
Long version
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N. N. Schraudolph and D. Kamenetsky.
Efficient Exact Inference in Planar Ising Models. Technical Report 0810.4401,
arXiv, 2008.
Short
version
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Karatzoglou, Alexandros
A. Karatzoglou, S. Vishwanathan,
N. N. Schraudolph, and A.
J. Smola. Step Size-Adapted Online Support Vector Learning.
In Proc. 8th Intl. Symp. Signal Processing & Applications, IEEE,
2005.
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Klapper-Rybicka, Magdalena
M. Klapper-Rybicka, N. N. Schraudolph, and
J. Schmidhuber. Unsupervised Learning in Recurrent Neural Networks.
In Proc. Intl. Conf. Artificial Neural Networks (ICANN), pp. 674–681,
Springer Verlag, Berlin, Vienna, Austria, 2001.
Details
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[pdf] [djvu] [ps.gz]
Koller-Meier, Esther
M. Bray, E. Koller-Meier, N. N. Schraudolph,
and L. Van Gool. Fast Stochastic Optimization for Articulated
Structure Tracking. Image and Vision Computing, 25(3):352–364, 2007.
Earlier version
Details
Download:
[pdf] [djvu] [ps.gz]
M. Bray, E. Koller-Meier, P. Müller, N.
N. Schraudolph, and L. Van Gool. Stochastic Optimization
for High-Dimensional Tracking in Dense Range Maps. IEE Proceedings
Vision, Image & Signal Processing, 152(4):501–512, 2005.
Earlier
version
Details
Download:
[pdf] [djvu] [ps.gz]
M. Bray, E. Koller-Meier, N. N. Schraudolph,
and L. Van Gool. Stochastic Meta-Descent for Tracking Articulated
Structures. In IEEE Workshop on Articulated and Nonrigid Motion,
Conference on Computer Vision and Pattern Recognition (CVPR), Washington, D.C.,
2004.
Latest version
Details
Download:
[pdf] [djvu] [ps.gz]
M. Bray, E. Koller-Meier, P. Müller, L. Van Gool, and N.
N. Schraudolph. 3D Hand Tracking by Rapid Stochastic Gradient
Descent Using a Skinning Model. In First European Conference
on Visual Media Production (CVMP), pp. 59–68, London, 2004.
Latest
version
Details
Download:
[pdf] [djvu] [ps.gz]
Kondor, Risi
S. Vishwanathan, N.
N. Schraudolph, R. Kondor, and K. Borgwardt. Graph Kernels.
Journal of Machine Learning Research,
11:1201–1242, 2010.
Short version
Details
Download:
[pdf] [djvu] [ps.gz]
Koumoutsakos, Petros
D. Büche, N. N. Schraudolph, and P.
Koumoutsakos. Accelerating Evolutionary Algorithms with
Gaussian Process Fitness Function Models. IEEE Transactions on Systems, Man,
and Cybernetics, C35(2):183–194, 2005.
Earlier
version
Details
Download:
[pdf] [djvu] [ps.gz]
D. Büche, N. N. Schraudolph, and P.
Koumoutsakos. Accelerating Evolutionary Algorithms Using
Fitness Function Models. In Genetic and Evolutionary Computation Conference
Workshop Program, pp. 166–169, AAAI, Chicago, 2003.
Latest
version
Details
Download:
[pdf] [djvu] [ps.gz]
S. Müller, N. N. Schraudolph, and P.
Koumoutsakos. Evolutionary and Gradient-Based Algorithms
for Lennard-Jones Cluster Optimization. In Genetic and Evolutionary Computation
Conference Workshop Program, pp. 160–165, AAAI, Chicago, 2003.
Details
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[pdf] [djvu] [ps.gz]
S. Müller, N. N. Schraudolph, P. Koumoutsakos,
and N. Hansen. Step Size Adaptation in Evolution Strategies---Two Approaches.
In Genetic and Evolutionary Computation Conference Workshop Program,
pp. 161–164, AAAI, New York, 2002.
Details
Download:
[pdf] [djvu] [ps.gz]
S. Müller, N. N. Schraudolph, and P.
D. Koumoutsakos. Step Size Adaptation in Evolution Strategies
using Reinforcement Learning. In Proc. Congress on Evolutionary Computation,
pp. 151–156, IEEE, 2002.
Details
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[pdf] [djvu] [ps.gz]
Kriegel, Hans-Peter
K. M. Borgwardt, H. Kriegel, S.
Vishwanathan, and N. N. Schraudolph.
Graph Kernels for Disease Outcome Prediction from
Protein-Protein Interaction Networks. In Proc. Pacific Symposium on Biocomputing
(PSB), pp. 4–15, World Scientific, Maui, Hawaii, 2007.
Details
Download:
[pdf] [djvu] [ps.gz]
Li, Zhidong
Z. Li, J. Chen, and N. N. Schraudolph.
An Improved Mean-Shift Tracker with Kernel Prediction and Scale Optimisation
Targeting for Low-Frame-Rate Video Tracking. In 19th
Intl. Conf. Pattern Recognition (ICPR), Tampa, Florida, 2008.
Details
Download:
[pdf] [djvu] [ps.gz]
Z. Li, J. Chen, A. Chong, Z. Yu, and N. N. Schraudolph.
Using Stochastic Gradient-Descent Scheme in Appearance
Model Based Face Tracking. In Proc. Intl. Workshop Multimedia Signal Processing
(MMSP), IEEE, Cairns, Australia, 2008.
Details
Download:
[pdf] [djvu] [ps.gz]
Müller, Pascal
M. Bray, E. Koller-Meier, P. Müller, N.
N. Schraudolph, and L. Van Gool. Stochastic Optimization
for High-Dimensional Tracking in Dense Range Maps. IEE Proceedings
Vision, Image & Signal Processing, 152(4):501–512, 2005.
Earlier
version
Details
Download:
[pdf] [djvu] [ps.gz]
M. Bray, E. Koller-Meier, P. Müller, L. Van Gool, and N.
N. Schraudolph. 3D Hand Tracking by Rapid Stochastic Gradient
Descent Using a Skinning Model. In First European Conference
on Visual Media Production (CVMP), pp. 59–68, London, 2004.
Latest
version
Details
Download:
[pdf] [djvu] [ps.gz]
Müller, Sybille
S. Müller, N. N. Schraudolph, and P.
Koumoutsakos. Evolutionary and Gradient-Based Algorithms
for Lennard-Jones Cluster Optimization. In Genetic and Evolutionary Computation
Conference Workshop Program, pp. 160–165, AAAI, Chicago, 2003.
Details
Download:
[pdf] [djvu] [ps.gz]
S. Müller, N. N. Schraudolph, P. Koumoutsakos,
and N. Hansen. Step Size Adaptation in Evolution Strategies---Two Approaches.
In Genetic and Evolutionary Computation Conference Workshop Program,
pp. 161–164, AAAI, New York, 2002.
Details
Download:
[pdf] [djvu] [ps.gz]
S. Müller, N. N. Schraudolph, and P.
D. Koumoutsakos. Step Size Adaptation in Evolution Strategies
using Reinforcement Learning. In Proc. Congress on Evolutionary Computation,
pp. 151–156, IEEE, 2002.
Details
Download:
[pdf] [djvu] [ps.gz]
McInerney, John
R. K. Belew, J. McInerney, and N. N. Schraudolph.
Evolving Networks: Using the Genetic Algorithm with
Connectionist Learning. In C. G. Langton, C. Taylor, J. D. Farmer, and S. Rasmussen,
editors, Artificial Life II, SFI Studies in the Sciences of Complexity: Proceedings,
pp. 511–547, Addison-Wesley, Redwood City, CA, 1992.
Details
Download:
[pdf] [djvu] [ps.gz]
Murphy, Kevin
S. Vishwanathan, N.
N. Schraudolph, M. W. Schmidt, and K. Murphy. Accelerated Training
of Conditional Random Fields with Stochastic Gradient Methods. In
Proc. 23rd Intl. Conf. Machine Learning (ICML), pp. 969–976,
ACM Press, 2006.
Details
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[pdf] [djvu] [ps.gz]
Rohr, Peter von
G. Cannarozzi, N. N. Schraudolph, M. Faty (joint first authors),
P. v. Rohr, M. T. Friberg, A. C. Roth, G. H. Gonnet, and Y. Barral.
A Role for Codon Order in Translation Dynamics. Cell,
141(2):355–367, 16 Apr 2010.
Details
Download:
[pdf] [djvu] [ps.gz]
Roth, Alexander C.
G. Cannarozzi, N. N. Schraudolph, M. Faty (joint first authors),
P. v. Rohr, M. T. Friberg, A. C. Roth, G. H. Gonnet, and Y. Barral.
A Role for Codon Order in Translation Dynamics. Cell,
141(2):355–367, 16 Apr 2010.
Details
Download:
[pdf] [djvu] [ps.gz]
Schmidhuber, Jürgen
F. A. Gers, N. N. Schraudolph, and J. Schmidhuber.
Learning Precise Timing with LSTM Recurrent Networks. Journal of Machine Learning Research, 3:115–143,
2002.
Details
Download:
[pdf] [djvu] [ps.gz]
M. Klapper-Rybicka, N. N. Schraudolph, and
J. Schmidhuber. Unsupervised Learning in Recurrent Neural Networks.
In Proc. Intl. Conf. Artificial Neural Networks (ICANN), pp. 674–681,
Springer Verlag, Berlin, Vienna, Austria, 2001.
Details
Download:
[pdf] [djvu] [ps.gz]
N. N. Schraudolph, M. Eldracher, and J.
Schmidhuber. Processing Images by Semi-Linear Predictability Minimization.
Network: Computation in Neural Systems, 10(2):133–169, 1999.
Details
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[djvu] [ps.gz]
J. Schmidhuber, J. Zhao, and N. N. Schraudolph.
Reinforcement Learning with Self-Modifying Policies. In S. Thrun
and L. Pratt, editors, Learning to Learn, pp. 293–309, Kluwer Academic
Publishers, Norwell, MA, 1998.
Details
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[pdf] [djvu] [ps.gz]
Schmidt, Mark W.
S. Vishwanathan, N.
N. Schraudolph, M. W. Schmidt, and K. Murphy. Accelerated Training
of Conditional Random Fields with Stochastic Gradient Methods. In
Proc. 23rd Intl. Conf. Machine Learning (ICML), pp. 969–976,
ACM Press, 2006.
Details
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[pdf] [djvu] [ps.gz]
Sejnowski, Terrence J.
N. N. Schraudolph, P. Dayan, and T.
J. Sejnowski. Learning to Evaluate Go Positions
via Temporal Difference Methods. In N. Baba and L. C. Jain, editors, Computational
Intelligence in Games, Studies in Fuzziness and Soft Computing, pp. 77–98,
Springer Verlag, Berlin, 2001.
Earlier
version
Details
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[pdf] [djvu] [ps.gz]
N. N. Schraudolph and T.
J. Sejnowski. Tempering Backpropagation Networks:
Not All Weights Are Created Equal. In Advances
in Neural Information Processing Systems (NIPS), pp. 563–569, The MIT
Press, Cambridge, MA, 1996.
Details
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[pdf] [djvu] [ps.gz]
P. A. Viola, N. N. Schraudolph, and T.
J. Sejnowski. Empirical Entropy Manipulation for Real-World
Problems. In Advances in Neural Information
Processing Systems (NIPS), pp. 851–857, The MIT Press, Cambridge, MA,
1996.
In Ph.D. thesis Latest version
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N. N. Schraudolph and T.
J. Sejnowski. Plasticity-Mediated Competitive Learning.
In Advances in Neural Information Processing
Systems (NIPS), pp. 475–480, The MIT Press, Cambridge, MA, 1995.
In Ph.D. thesis
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N. N. Schraudolph, P. Dayan, and T.
J. Sejnowski. Temporal Difference Learning of Position Evaluation
in the Game of Go. In Advances in Neural
Information Processing Systems (NIPS), pp. 817–824, Morgan Kaufmann,
San Francisco, CA, 1994.
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N. N. Schraudolph and T.
J. Sejnowski. Unsupervised Discrimination of Clustered Data
via Optimization of Binary Information Gain. In
Advances in Neural Information Processing Systems (NIPS), pp. 499–506,
Morgan Kaufmann, San Mateo, CA, 1993.
In Ph.D.
thesis
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N. N. Schraudolph and T.
J. Sejnowski. Competitive Anti-Hebbian Learning of Invariants.
In Advances in Neural Information Processing
Systems (NIPS), pp. 1017–1024, Morgan Kaufmann, San Mateo, CA, 1992.
In Ph.D. thesis
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Smola, Alex J.
S. Vishwanathan, N.
N. Schraudolph, and A. J. Smola.
Step Size Adaptation in Reproducing Kernel Hilbert Space.
Journal of Machine Learning Research, 7:1107–1133, 2006.
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A. Karatzoglou, S. Vishwanathan,
N. N. Schraudolph, and A.
J. Smola. Step Size-Adapted Online Support Vector Learning.
In Proc. 8th Intl. Symp. Signal Processing & Applications, IEEE,
2005.
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Sunehag, Peter
P. Sunehag, J. Trumpf, S. Vishwanathan,
and N. N. Schraudolph.
Variable Metric Stochastic Approximation Theory. In Proc. 12th Intl.
Conf. Artificial Intelligence and Statistics (AIstats), pp. 560–566,
Journal of Machine Learning Research, Clearwater
Beach, Florida, 2009.
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Trumpf, Jochen
P. Sunehag, J. Trumpf, S. Vishwanathan,
and N. N. Schraudolph.
Variable Metric Stochastic Approximation Theory. In Proc. 12th Intl.
Conf. Artificial Intelligence and Statistics (AIstats), pp. 560–566,
Journal of Machine Learning Research, Clearwater
Beach, Florida, 2009.
Details
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D. Chik, J. Trumpf, and N. N. Schraudolph.
Using an Adaptive VAR Model for Motion Prediction
in 3D Hand Tracking. In 8th Intl. Conf. Automatic
Face & Gesture Recognition (FG), IEEE, Amsterdam, Netherlands, 2008.
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D. Chik, J. Trumpf, and N. N. Schraudolph.
3D Hand Tracking in a Stochastic Approximation Setting. In 2nd
Workshop on Human Motion: Understanding, Modeling, Capture and Animation,
11th IEEE Intl. Conf. Computer Vision (ICCV), pp. 136–151,
Springer Verlag, Berlin, Rio de Janeiro, Brazil,
2007.
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Van Gool, Luc
M. Bray, E. Koller-Meier, N. N. Schraudolph,
and L. Van Gool. Fast Stochastic Optimization for Articulated
Structure Tracking. Image and Vision Computing, 25(3):352–364, 2007.
Earlier version
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M. Bray, E. Koller-Meier, P. Müller, N.
N. Schraudolph, and L. Van Gool. Stochastic Optimization
for High-Dimensional Tracking in Dense Range Maps. IEE Proceedings
Vision, Image & Signal Processing, 152(4):501–512, 2005.
Earlier
version
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M. Bray, E. Koller-Meier, N. N. Schraudolph,
and L. Van Gool. Stochastic Meta-Descent for Tracking Articulated
Structures. In IEEE Workshop on Articulated and Nonrigid Motion,
Conference on Computer Vision and Pattern Recognition (CVPR), Washington, D.C.,
2004.
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M. Bray, E. Koller-Meier, P. Müller, L. Van Gool, and N.
N. Schraudolph. 3D Hand Tracking by Rapid Stochastic Gradient
Descent Using a Skinning Model. In First European Conference
on Visual Media Production (CVMP), pp. 59–68, London, 2004.
Latest
version
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Viola, Paul A.
P. A. Viola, N. N. Schraudolph, and T.
J. Sejnowski. Empirical Entropy Manipulation for Real-World
Problems. In Advances in Neural Information
Processing Systems (NIPS), pp. 851–857, The MIT Press, Cambridge, MA,
1996.
In Ph.D. thesis Latest version
Details
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[pdf] [djvu] [ps.gz]
Vishwanathan, S. V. N.
S. Vishwanathan, N.
N. Schraudolph, R. Kondor, and K. Borgwardt. Graph Kernels.
Journal of Machine Learning Research,
11:1201–1242, 2010.
Short version
Details
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J. Yu, S. Vishwanathan, S.
Günter, and N. N. Schraudolph.
A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in
Machine Learning. Journal of Machine Learning
Research, 11:1145–1200, 2010.
Short
version
Details
Download:
[pdf] [djvu] [ps.gz]
P. Sunehag, J. Trumpf, S. Vishwanathan,
and N. N. Schraudolph.
Variable Metric Stochastic Approximation Theory. In Proc. 12th Intl.
Conf. Artificial Intelligence and Statistics (AIstats), pp. 560–566,
Journal of Machine Learning Research, Clearwater
Beach, Florida, 2009.
Details
Download:
[pdf] [djvu] [ps.gz]
J. Yu, S. Vishwanathan, S.
Günter, and N. N. Schraudolph.
A Quasi-Newton Approach to Nonsmooth Convex Optimization. In Proc. 25th
Intl. Conf. Machine Learning (ICML), pp. 1216–1223, Omnipress, Helsinki,
Finland, 2008.
Long version
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K. M. Borgwardt, H. Kriegel, S.
Vishwanathan, and N. N. Schraudolph.
Graph Kernels for Disease Outcome Prediction from
Protein-Protein Interaction Networks. In Proc. Pacific Symposium on Biocomputing
(PSB), pp. 4–15, World Scientific, Maui, Hawaii, 2007.
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S. Günter, N. N. Schraudolph, and S. Vishwanathan.
Fast Iterative Kernel Principal Component Analysis.
Journal of Machine Learning Research, 8:1893–1918, 2007.
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N. N. Schraudolph, S. Günter, and S. Vishwanathan.
Fast Iterative Kernel PCA. In Advances in
Neural Information Processing Systems (NIPS), pp. 1225–1232, MIT Press,
Cambridge, MA, 2007.
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S. Vishwanathan, K. Borgwardt,
and N. N. Schraudolph.
Fast Computation of Graph Kernels. In Advances
in Neural Information Processing Systems (NIPS), pp. 1449–1456, MIT
Press, Cambridge, MA, 2007.
Long version
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S. Vishwanathan, N.
N. Schraudolph, M. W. Schmidt, and K. Murphy. Accelerated Training
of Conditional Random Fields with Stochastic Gradient Methods. In
Proc. 23rd Intl. Conf. Machine Learning (ICML), pp. 969–976,
ACM Press, 2006.
Details
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S. Vishwanathan, N.
N. Schraudolph, and A. J. Smola.
Step Size Adaptation in Reproducing Kernel Hilbert Space.
Journal of Machine Learning Research, 7:1107–1133, 2006.
Details
Download:
[pdf] [djvu] [ps.gz]
A. Karatzoglou, S. Vishwanathan,
N. N. Schraudolph, and A.
J. Smola. Step Size-Adapted Online Support Vector Learning.
In Proc. 8th Intl. Symp. Signal Processing & Applications, IEEE,
2005.
Details
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[pdf] [djvu] [ps.gz]
Yu, Jin
J. Yu, S. Vishwanathan, S.
Günter, and N. N. Schraudolph.
A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in
Machine Learning. Journal of Machine Learning
Research, 11:1145–1200, 2010.
Short
version
Details
Download:
[pdf] [djvu] [ps.gz]
J. Yu, S. Vishwanathan, S.
Günter, and N. N. Schraudolph.
A Quasi-Newton Approach to Nonsmooth Convex Optimization. In Proc. 25th
Intl. Conf. Machine Learning (ICML), pp. 1216–1223, Omnipress, Helsinki,
Finland, 2008.
Long version
Details
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[pdf] [djvu] [ps.gz]
N. N. Schraudolph, J. Yu, and S. Günter.
A Stochastic Quasi-Newton Method for Online Convex
Optimization. In Proc. 11th Intl. Conf. Artificial
Intelligence and Statistics (AIstats), pp. 436–443,
Journal of Machine Learning Research, San Juan, Puerto Rico, 2007.
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N. N. Schraudolph, J. Yu, and D. Aberdeen.
Fast Online Policy Gradient Learning with SMD Gain
Vector Adaptation. In Advances in Neural
Information Processing Systems (NIPS), pp. 1185–1192, MIT Press, Cambridge,
MA, 2006.
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Yu, Zhenghua
Z. Li, J. Chen, A. Chong, Z. Yu, and N. N. Schraudolph.
Using Stochastic Gradient-Descent Scheme in Appearance
Model Based Face Tracking. In Proc. Intl. Workshop Multimedia Signal Processing
(MMSP), IEEE, Cairns, Australia, 2008.
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Zhao, Jieyu
J. Schmidhuber, J. Zhao, and N. N. Schraudolph.
Reinforcement Learning with Self-Modifying Policies. In S. Thrun
and L. Pratt, editors, Learning to Learn, pp. 293–309, Kluwer Academic
Publishers, Norwell, MA, 1998.
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