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Conferences in DBLP

Neural Information Processing Systems (NIPS) (nips)
1999 (conf/nips/1999)

  1. Jessica D. Bayliss, Dana H. Ballard
    Recognizing Evoked Potentials in a Virtual Environment. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:3-9 [Conf]
  2. Gustavo Deco, Josef Zihl
    A Neurodynamical Approach to Visual Attention. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:10-16 [Conf]
  3. Thea B. Ghiselli-Crippa, Paul W. Munro
    Effects of Spatial and Temporal Contiguity on the Acquisition of Spatial Information. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:17-23 [Conf]
  4. Sham Kakade, Peter Dayan
    Acquisition in Autoshaping. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:24-30 [Conf]
  5. Soo-Young Lee, Michael Mozer
    Robust Recognition of Noisy and Superimposed Patterns via Selective Attention. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:31-37 [Conf]
  6. Xiuwen Liu, DeLiang L. Wang
    Perceptual Organization Based on Temporal Dynamics. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:38-44 [Conf]
  7. Javier R. Movellan, James L. McClelland
    Information Factorization in Connectionist Models of Perception. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:45-51 [Conf]
  8. Shan Parfitt, Peter Tiño, Georg Dorffner
    Graded Grammaticality in Prediction Fractal Machines. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:52-58 [Conf]
  9. Joshua B. Tenenbaum
    Rules and Similarity in Concept Learning. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:59-65 [Conf]
  10. Bradley Tonkes, Alan D. Blair, Janet Wiles
    Evolving Learnable Languages. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:66-72 [Conf]
  11. Ton Weijters, Antal van den Bosch, Eric O. Postma
    Learning Statistically Neutral Tasks without Expert Guidance. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:73-79 [Conf]
  12. Richard S. Zemel, Michael Mozer
    A Generative Model for Attractor Dynamics. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:80-88 [Conf]
  13. Péter Adorján, Lars Schwabe, Christian Piepenbrock, Klaus Obermayer
    Recurrent Cortical Competition: Strengthen or Weaken? [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:89-95 [Conf]
  14. Gal Chechik, Isaac Meilijson, Eytan Ruppin
    Effective Learning Requires Neuronal Remodeling of Hebbian Synapses. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:96-102 [Conf]
  15. Dmitri B. Chklovskii, Charles F. Stevens
    Wiring Optimization in the Brain [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:103-107 [Conf]
  16. Dmitri B. Chklovskii
    Optimal Sizes of Dendritic and Axonal Arbors. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:108-114 [Conf]
  17. Christian W. Eurich, Stefan D. Wilke, Helmut Schwegler
    Neural Representation of Multi-Dimensional Stimuli. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:115-121 [Conf]
  18. Geoffrey E. Hinton, Andrew D. Brown
    Spiking Boltzmann Machines. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:122-128 [Conf]
  19. David Horn, Nir Levy, Isaac Meilijson, Eytan Ruppin
    Distributed Synchrony of Spiking Neurons in a Hebbian Cell Assembly. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:129-135 [Conf]
  20. Zhaoping Li
    Can VI Mechanisms Account for Figure-Ground and Medial Axis Effects? [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:136-142 [Conf]
  21. Amit Manwani, Peter N. Steinmetz, Christof Koch
    Channel Noise in Excitable Neural Membranes. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:143-149 [Conf]
  22. Paul W. Munro, Gerardina Hernandez
    LTD Facilitates Learning in a Noisy Environment. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:150-156 [Conf]
  23. Panayiota Poirazi, Bartlett W. Mel
    Memory Capacity of Linear vs. Nonlinear Models of Dendritic Integration. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:157-163 [Conf]
  24. Rajesh P. N. Rao, Terrence J. Sejnowski
    Predictive Sequence Learning in Recurrent Neocortical Circuits. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:164-170 [Conf]
  25. Alfonso Renart, Néstor Parga, Edmund T. Rolls
    A Recurrent Model of the Interaction Between Prefrontal and Inferotemporal Cortex in Delay Tasks. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:171-177 [Conf]
  26. Elad Schneidman, Idan Segev, Naftali Tishby
    Information Capacity and Robustness of Stochastic Neuron Models. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:178-184 [Conf]
  27. Akaysha C. Tang, Barak A. Pearlmutter, Tim A. Hely, Michael Zibulevsky, Michael P. Weisend
    An MEG Study of Response Latency and Variability in the Human Visual System During a Visual-Motor Integration Task. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:185-191 [Conf]
  28. Si Wu, Hiroyuki Nakahara, Noboru Murata, Shun-ichi Amari
    Population Decoding Based on an Unfaithful Model. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:192-198 [Conf]
  29. Xiaohui Xie, H. Sebastian Seung
    Spike-based Learning Rules and Stabilization of Persistent Neural Activity. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:199-208 [Conf]
  30. Hagai Attias
    A Variational Baysian Framework for Graphical Models. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:209-215 [Conf]
  31. Joachim M. Buhmann, Marcus Held
    Model Selection in Clustering by Uniform Convergence Bounds. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:216-222 [Conf]
  32. Christopher J. C. Burges, David J. Crisp
    Uniqueness of the SVM Solution. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:223-229 [Conf]
  33. Olivier Chapelle, Vladimir Vapnik
    Model Selection for Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:230-236 [Conf]
  34. Anthony C. C. Coolen, C. W. H. Mace
    Dynamics of Supervised Learning with Restricted Training Sets and Noisy Teachers. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:237-243 [Conf]
  35. David J. Crisp, Christopher J. C. Burges
    A Geometric Interpretation of v-SVM Classifiers. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:244-250 [Conf]
  36. Lehel Csató, Ernest Fokoué, Manfred Opper, Bernhard Schottky, Ole Winther
    Efficient Approaches to Gaussian Process Classification. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:251-257 [Conf]
  37. Nigel Duffy, David P. Helmbold
    Potential Boosters? [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:258-264 [Conf]
  38. Lars Kai Hansen
    Bayesian Averaging is Well-Temperated. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:265-271 [Conf]
  39. Yoshiyuki Kabashima, Tatsuto Murayama, David Saad, Renato Vicente
    Regular and Irregular Gallager-zype Error-Correcting Codes. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:272-278 [Conf]
  40. Jonathan Q. Li, Andrew R. Barron
    Mixture Density Estimation. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:279-285 [Conf]
  41. Song Li, K. Y. Michael Wong
    Statistical Dynamics of Batch Learning. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:286-292 [Conf]
  42. Wolfgang Maass
    Neural Computation with Winner-Take-All as the Only Nonlinear Operation. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:293-299 [Conf]
  43. Yishay Mansour, David A. McAllester
    Boosting with Multi-Way Branching in Decision Trees. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:300-306 [Conf]
  44. Claude Nadeau, Yoshua Bengio
    Inference for the Generalization Error. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:307-313 [Conf]
  45. Toru Ohira, Yuzuru Sato, Jack D. Cowan
    Resonance in a Stochastic Neuron Model with Delayed Interaction. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:314-320 [Conf]
  46. Sebastian Risau-Gusman, Mirta B. Gordon
    Understanding Stepwise Generalization of Support Vector Machines: a Toy Model. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:321-327 [Conf]
  47. Michael Schmitt
    Lower Bounds on the Complexity of Approximating Continuous Functions by Sigmoidal Neural Networks. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:328-334 [Conf]
  48. Hava T. Siegelmann, Alexander Roitershtein, Asa Ben-Hur
    Noisy Neural Networks and Generalizations. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:335-341 [Conf]
  49. Alex J. Smola, John Shawe-Taylor, Bernhard Schölkopf, Robert C. Williamson
    The Entropy Regularization Information Criterion. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:342-348 [Conf]
  50. Peter Sollich
    Probabilistic Methods for Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:349-355 [Conf]
  51. Sumio Watanabe
    Algebraic Analysis for Non-regular Learning Machines. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:356-362 [Conf]
  52. Liqing Zhang, Shun-ichi Amari, Andrzej Cichocki
    Semiparametric Approach to Multichannel Blind Deconvolution of Nonminimum Phase Systems [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:363-369 [Conf]
  53. Tong Zhang
    Some Theoretical Results Concerning the Convergence of Compositions of Regularized Linear Functions. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:370-378 [Conf]
  54. Christophe Andrieu, João F. G. de Freitas, Arnaud Doucet
    Robust Full Bayesian Methods for Neural Networks. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:379-385 [Conf]
  55. Hagai Attias
    Independent Factor Analysis with Temporally Structured Sources. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:386-392 [Conf]
  56. David Barber, Peter Sollich
    Gaussian Fields for Approximate Inference in Layered Sigmoid Belief Networks. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:393-399 [Conf]
  57. Yoshua Bengio, Samy Bengio
    Modeling High-Dimensional Discrete Data with Multi-Layer Neural Networks. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:400-406 [Conf]
  58. Thomas Briegel, Volker Tresp
    Robust Neural Network Regression for Offline and Online Learning. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:407-413 [Conf]
  59. Miguel Á. Carreira-Perpiñán
    Reconstruction of Sequential Data with Probabilistic Models and Continuity Constraints. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:414-420 [Conf]
  60. Olivier Chapelle, Vladimir Vapnik, Jason Weston
    Transductive Inference for Estimating Values of Functions. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:421-427 [Conf]
  61. Oliver B. Downs, David J. C. MacKay, Daniel D. Lee
    The Nonnegative Boltzmann Machine. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:428-434 [Conf]
  62. Gary William Flake, Barak A. Pearlmutter
    Differentiating Functions of the Jacobian with Respect to the Weights. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:435-441 [Conf]
  63. Brendan J. Frey
    Local Probability Propagation for Factor Analysis. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:442-448 [Conf]
  64. Zoubin Ghahramani, Matthew J. Beal
    Variational Inference for Bayesian Mixtures of Factor Analysers. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:449-455 [Conf]
  65. Thore Graepel, Ralf Herbrich, Klaus Obermayer
    Bayesian Transduction. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:456-462 [Conf]
  66. Geoffrey E. Hinton, Zoubin Ghahramani, Yee Whye Teh
    Learning to Parse Images. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:463-469 [Conf]
  67. Tommi Jaakkola, Marina Meila, Tony Jebara
    Maximum Entropy Discrimination. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:470-476 [Conf]
  68. Nebojsa Jojic, Brendan J. Frey
    Topographic Transformation as a Discrete Latent Variable. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:477-483 [Conf]
  69. Pavel Laskov
    An Improved Decomposition Algorithm for Regression Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:484-490 [Conf]
  70. Daniel D. Lee, Uri Rokni, Haim Sompolinsky
    Algorithms for Independent Components Analysis and Higher Order Statistics. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:491-497 [Conf]
  71. Yi Li, Philip M. Long
    The Relaxed Online Maximum Margin Algorithm. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:498-504 [Conf]
  72. Dimitris Margaritis, Sebastian Thrun
    Bayesian Network Induction via Local Neighborhoods. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:505-511 [Conf]
  73. Llew Mason, Jonathan Baxter, Peter L. Bartlett, Marcus R. Frean
    Boosting Algorithms as Gradient Descent. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:512-518 [Conf]
  74. Chris Mesterharm
    A Multi-class Linear Learning Algorithm Related to Winnow. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:519-525 [Conf]
  75. Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller
    Invariant Feature Extraction and Classification in Kernel Spaces. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:526-532 [Conf]
  76. Andrew Y. Ng, Michael I. Jordan
    Approximate Inference A lgorithms for Two-Layer Bayesian Networks. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:533-539 [Conf]
  77. Dirk Ormoneit, Trevor Hastie
    Optimal Kernel Shapes for Local Linear Regression. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:540-546 [Conf]
  78. John C. Platt, Nello Cristianini, John Shawe-Taylor
    Large Margin DAGs for Multiclass Classification. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:547-553 [Conf]
  79. Carl Edward Rasmussen
    The Infinite Gaussian Mixture Model. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:554-560 [Conf]
  80. Gunnar Rätsch, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller, Takashi Onoda, Sebastian Mika
    v-Arc: Ensemble Learning in the Presence of Outliers. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:561-567 [Conf]
  81. Volker Roth, Volker Steinhage
    Nonlinear Discriminant Analysis Using Kernel Functions. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:568-574 [Conf]
  82. Paat Rusmevichientong, Benjamin Van Roy
    An Analysis of Turbo Decoding with Gaussian Densities. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:575-581 [Conf]
  83. Bernhard Schölkopf, Robert C. Williamson, Alex J. Smola, John Shawe-Taylor, John C. Platt
    Support Vector Method for Novelty Detection. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:582-588 [Conf]
  84. Mike Schuster
    Better Generative Models for Sequential Data Problems: Bidirectional Recurrent Mixture Density Networks. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:589-595 [Conf]
  85. Dale Schuurmans
    Greedy Importance Sampling. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:596-602 [Conf]
  86. Matthias Seeger
    Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:603-609 [Conf]
  87. Yoram Singer
    Leveraged Vector Machines. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:610-616 [Conf]
  88. Noam Slonim, Naftali Tishby
    Agglomerative Information Bottleneck. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:617-623 [Conf]
  89. Masashi Sugiyama, Hidemitsu Ogawa
    Training Data Selection for Optimal Generalization in Trigonometric Polynomial Networks. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:624-630 [Conf]
  90. S. Sundararajan, S. Sathiya Keerthi
    Predictive App roaches for Choosing Hyperparameters in Gaussian Processes. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:631-637 [Conf]
  91. Peter Sykacek
    On Input Selection with Reversible Jump Markov Chain Monte Carlo Sampling. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:638-644 [Conf]
  92. Peter Tiño, Georg Dorffner
    Building Predictive Models from Fractal Representations of Symbolic Sequences. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:645-651 [Conf]
  93. Michael E. Tipping
    The Relevance Vector Machine. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:652-658 [Conf]
  94. Vladimir Vapnik, Sayan Mukherjee
    Support Vector Method for Multivariate Density Estimation. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:659-665 [Conf]
  95. Eric A. Wan, Rudolph van der Merwe, Alex T. Nelson
    Dual Estimation and the Unscented Transformation. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:666-672 [Conf]
  96. Yair Weiss, William T. Freeman
    Correctness of Belief Propagation in Gaussian Graphical Models of Arbitrary Topology. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:673-679 [Conf]
  97. Christopher K. I. Williams
    A MCMC Approach to Hierarchical Mixture Modelling. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:680-686 [Conf]
  98. Howard Hua Yang, John E. Moody
    Data Visualization and Feature Selection: New Algorithms for Nongaussian Data. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:687-702 [Conf]
  99. Mark Zlochin, Yoram Baram
    Manifold Stochastic Dynamics for Bayesian Learning. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:694-702 [Conf]
  100. Charles Lee Isbell Jr., Parry Husbands
    The Parallel Problems Server: an Interactive Tool for Large Scale Machine Learning. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:703-709 [Conf]
  101. Oliver Landolt, Steve Gyger
    An Oculo-Motor System with Multi-Chip Neuromorphic Analog VLSI Control. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:710-716 [Conf]
  102. Shih-Chii Liu
    A Winner-Take-All Circuit with Controllable Soft Max Property. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:717-723 [Conf]
  103. Girish N. Patel, Edgar A. Brown, Stephen P. DeWeerth
    A Neuromorphic VLSI System for Modeling the Neural Control of Axial Locomotion. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:724-730 [Conf]
  104. Girish N. Patel, Gennady S. Cymbalyuk, Ronald L. Calabrese, Stephen P. DeWeerth
    Bifurcation Analysis of a Silicon Neuron. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:731-737 [Conf]
  105. André van Schaik
    An Analog VLSI Model of Periodicity Extraction. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:738-746 [Conf]
  106. Guy J. Brown, DeLiang L. Wang
    An Oscillatory Correlation Frame work for Computational Auditory Scene Analysis. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:747-753 [Conf]
  107. Pedro A. d. F. R. Højen-Sørensen, Lars Kai Hansen, Carl Edward Rasmussen
    Bayesian Modelling of fMRI lime Series. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:754-760 [Conf]
  108. Craig T. Jin, Simon Carlile
    Neural System Model of Human Sound Localization. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:761-767 [Conf]
  109. Craig T. Jin, Anna Corderoy, Simon Carlile, André van Schaik
    Spectral Cues in Human Sound Localization. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:768-774 [Conf]
  110. Justinian P. Rosca, Joseph Ó Ruanaidh, Alexander Jourjine, Scott Rickard
    Broadband Direction-Of-Arrival Estimation Based on Second Order Statistics. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:775-781 [Conf]
  111. Sam T. Roweis
    Constrained Hidden Markov Models. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:782-788 [Conf]
  112. Nicol N. Schraudolph, Xavier Giannakopoulos
    Online Independent Component Analysis with Local Learning Rate Adaptation. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:789-795 [Conf]
  113. Gavin Smith, João F. G. de Freitas, Tony Robinson, Mahesan Niranjan
    Speech Modelling Using Subspace and EM Techniques. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:796-802 [Conf]
  114. Howard Hua Yang, Hynek Hermansky
    Search for Information Bearing Components in Speech. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:803-812 [Conf]
  115. John Hershey, Javier R. Movellan
    Audio Vision: Using Audio-Visual Synchrony to Locate Sounds. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:813-819 [Conf]
  116. Nicholas R. Howe, Michael E. Leventon, William T. Freeman
    Bayesian Reconstruction of 3D Human Motion from Single-Camera Video. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:820-826 [Conf]
  117. Aapo Hyvärinen, Patrik O. Hoyer
    Emergence of Topography and Complex Cell Properties from Natural Images using Extensions of ICA. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:827-833 [Conf]
  118. Tai Sing Lee, Stella X. Yu
    An Information-Theoretic Framework for Understanding Saccadic Eye Movements. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:834-840 [Conf]
  119. Bruno A. Olshausen, K. Jarrod Millman
    Learning Sparse Codes with a Mixture-of-Gaussians Prior. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:841-847 [Conf]
  120. Clay Spence, Lucas C. Parra
    Hierarchical Image Probability (H1P) Models. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:848-854 [Conf]
  121. Martin J. Wainwright, Eero P. Simoncelli
    Scale Mixtures of Gaussians and the Statistics of Natural Images. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:855-861 [Conf]
  122. Ming-Hsuan Yang, Dan Roth, Narendra Ahuja
    A SNoW-Based Face Detector. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:862-868 [Conf]
  123. Zhiyong Yang, Richard S. Zemel
    Managing Uncertainty in Cue Combination. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:869-878 [Conf]
  124. Rembrandt Bakker, Jaap C. Schouten, Marc-Olivier Coppens, Floris Takens, C. Lee Giles, Cor M. van den Bleek
    Robust Learning of Chaotic Attractors. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:879-885 [Conf]
  125. Marian Stewart Bartlett, Gianluca Donato, Javier R. Movellan, Joseph C. Hager, Paul Ekman, Terrence J. Sejnowski
    Image Representations for Facial Expression Coding. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:886-892 [Conf]
  126. Timothy X. Brown
    Low Power Wireless Communication via Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:893-899 [Conf]
  127. John W. Fisher III, Alexander T. Ihler, Paul A. Viola
    Learning Informative Statistics: A Nonparametnic Approach. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:900-906 [Conf]
  128. Richard M. Golden
    Kirchoff Law Markov Fields for Analog Circuit Design. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:907-913 [Conf]
  129. Thomas Hofmann
    Learning the Similarity of Documents: An Information-Geometric Approach to Document Retrieval and Categorization. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:914-920 [Conf]
  130. Yuansong Liao, John E. Moody
    Constructing Heterogeneous Committees Using Input Feature Grouping: Application to Economic Forecasting. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:921-927 [Conf]
  131. Eric Mjolsness, Tobias Mann, Rebecca Castaño, Barbara J. Wold
    From Coexpression to Coregulation: An Approach to Inferring Transcriptional Regulation among Gene Classes from Large-Scale Expression Data. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:928-934 [Conf]
  132. Michael Mozer, Richard H. Wolniewicz, David B. Grimes, Eric Johnson, Howard Kaushansky
    Churn Reduction in the Wireless Industry. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:935-941 [Conf]
  133. Lucas C. Parra, Clay Spence, Paul Sajda, Andreas Ziehe, Klaus-Robert Müller
    Unmixing Hyperspectral Data. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:942-948 [Conf]
  134. Holger Schoner, Martin Stetter, Ingo Schießl, John E. W. Mayhew, Jennifer S. Lund, Niall McLoughlin, Klaus Obermayer
    Application of Blind Separation of Sources to Optical Recording of Brain Activity. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:949-955 [Conf]
  135. Satinder P. Singh, Michael J. Kearns, Diane J. Litman, Marilyn A. Walker
    Reinforcement Learning for Spoken Dialogue Systems. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:956-962 [Conf]
  136. Xubo B. Song, Joseph Sill, Yaser S. Abu-Mostafa, Harvey Kasdan
    Image Recognition in Context: Application to Microscopic Urinalysis. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:963-969 [Conf]
  137. Shivakumar Vaithyanathan, Byron Dom
    Generalized Model Selection for Unsupervised Learning in High Dimensions. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:970-976 [Conf]
  138. Nuno Vasconcelos, Andrew Lippman
    Learning from User Feedback in Image Retrieval Systems. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:977-986 [Conf]
  139. Samuel P. M. Choi, Dit-Yan Yeung, Nevin Lianwen Zhang
    An Environment Model for Nonstationary Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:987-993 [Conf]
  140. Thomas G. Dietterich
    State Abstraction in MAXQ Hierarchical Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:994-1000 [Conf]
  141. Michael J. Kearns, Yishay Mansour, Andrew Y. Ng
    Approximate Planning in Large POMDPs via Reusable Trajectories. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:1001-1007 [Conf]
  142. Vijay R. Konda, John N. Tsitsiklis
    Actor-Critic Algorithms. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:1008-1014 [Conf]
  143. Kevin P. Murphy
    Bayesian Map Learning in Dynamic Environments. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:1015-1021 [Conf]
  144. Andrew Y. Ng, Ronald Parr, Daphne Koller
    Policy Search via Density Estimation. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:1022-1028 [Conf]
  145. Stephen Piche, James D. Keeler, Greg Martin, Gene Boe, Doug Johnson, Mark Gerules
    Neural Network Based Model Predictive Control. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:1029-1035 [Conf]
  146. Andrés Rodríguez, Ronald Parr, Daphne Koller
    Reinforcement Learning Using Approximate Belief States. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:1036-1042 [Conf]
  147. Nicholas Roy, Sebastian Thrun
    Coastal Navigation with Mobile Robots. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:1043-1049 [Conf]
  148. Brian Sallans
    Learning Factored Representations for Partially Observable Markov Decision Processes. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:1050-1056 [Conf]
  149. Richard S. Sutton, David A. McAllester, Satinder P. Singh, Yishay Mansour
    Policy Gradient Methods for Reinforcement Learning with Function Approximation. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:1057-1063 [Conf]
  150. Sebastian Thrun
    Monte Carlo POMDPs. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:1064-1070 [Conf]
NOTICE1
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NOTICE2
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