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Richard S. Zemel: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Jasper Snoek, Jesse Hoey, Liam Stewart, Richard S. Zemel
    Automated Detection of Unusual Events on Stairs. [Citation Graph (0, 0)][DBLP]
    CRV, 2006, pp:5- [Conf]
  2. Xuming He, Richard S. Zemel, Miguel Á. Carreira-Perpiñán
    Multiscale Conditional Random Fields for Image Labeling. [Citation Graph (0, 0)][DBLP]
    CVPR (2), 2004, pp:695-702 [Conf]
  3. Xuming He, Richard S. Zemel, Debajyoti Ray
    Learning and Incorporating Top-Down Cues in Image Segmentation. [Citation Graph (0, 0)][DBLP]
    ECCV (1), 2006, pp:338-351 [Conf]
  4. Benjamin Marlin, Richard S. Zemel
    The multiple multiplicative factor model for collaborative filtering. [Citation Graph (0, 0)][DBLP]
    ICML, 2004, pp:- [Conf]
  5. David A. Ross, Simon Osindero, Richard S. Zemel
    Combining discriminative features to infer complex trajectories. [Citation Graph (0, 0)][DBLP]
    ICML, 2006, pp:761-768 [Conf]
  6. Richard S. Zemel, Peter Dayan
    Combining Probabilistic Population Codes. [Citation Graph (0, 0)][DBLP]
    IJCAI, 1997, pp:1114-1119 [Conf]
  7. Miguel Á. Carreira-Perpiñán, Richard S. Zemel
    Proximity Graphs for Clustering and Manifold Learning. [Citation Graph (0, 0)][DBLP]
    NIPS, 2004, pp:- [Conf]
  8. Michael S. Gray, Alexandre Pouget, Richard S. Zemel, Steven J. Nowlan, Terrence J. Sejnowski
    Selective Integration: A Model for Disparity Estimation. [Citation Graph (0, 0)][DBLP]
    NIPS, 1996, pp:866-872 [Conf]
  9. Geoffrey E. Hinton, Richard S. Zemel
    Autoencoders, Minimum Description Length and Helmholtz Free Energy. [Citation Graph (0, 0)][DBLP]
    NIPS, 1993, pp:3-10 [Conf]
  10. Michael Mozer, Richard S. Zemel, Marlene Behrmann
    Learning to Segment Images Using Dynamic Feature Binding. [Citation Graph (0, 0)][DBLP]
    NIPS, 1991, pp:436-443 [Conf]
  11. David A. Ross, Richard S. Zemel
    Multiple Cause Vector Quantization. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:1017-1024 [Conf]
  12. Max Welling, Richard S. Zemel, Geoffrey E. Hinton
    Self Supervised Boosting. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:665-672 [Conf]
  13. Zhiyong Yang, Richard S. Zemel
    Managing Uncertainty in Cue Combination. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:869-878 [Conf]
  14. Richard S. Zemel, Peter Dayan
    Distributional Population Codes and Multiple Motion Models. [Citation Graph (0, 0)][DBLP]
    NIPS, 1998, pp:174-182 [Conf]
  15. Richard S. Zemel, Peter Dayan, Alexandre Pouget
    Probabilistic Interpretation of Population Codes. [Citation Graph (0, 0)][DBLP]
    NIPS, 1996, pp:676-684 [Conf]
  16. Richard S. Zemel, Geoffrey E. Hinton
    Discovering Viewpoint-Invariant Relationships That Characterize Objects. [Citation Graph (0, 0)][DBLP]
    NIPS, 1990, pp:299-305 [Conf]
  17. Richard S. Zemel, Geoffrey E. Hinton
    Developing Population Codes by Minimizing Description Length. [Citation Graph (0, 0)][DBLP]
    NIPS, 1993, pp:11-18 [Conf]
  18. Richard S. Zemel, Quentin J. M. Huys, Rama Natarajan, Peter Dayan
    Probabilistic Computation in Spiking Populations. [Citation Graph (0, 0)][DBLP]
    NIPS, 2004, pp:- [Conf]
  19. Richard S. Zemel, Michael Mozer
    A Generative Model for Attractor Dynamics. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:80-88 [Conf]
  20. Richard S. Zemel, Michael Mozer, Geoffrey E. Hinton
    TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations. [Citation Graph (0, 0)][DBLP]
    NIPS, 1989, pp:266-273 [Conf]
  21. Richard S. Zemel, Toniann Pitassi
    A Gradient-Based Boosting Algorithm for Regression Problems. [Citation Graph (0, 0)][DBLP]
    NIPS, 2000, pp:696-702 [Conf]
  22. Richard S. Zemel, Terrence J. Sejnowski
    Grouping Components of Three-Dimensional Moving Objects in Area MST of Visual Cortex. [Citation Graph (0, 0)][DBLP]
    NIPS, 1994, pp:165-172 [Conf]
  23. Richard S. Zemel, Christopher K. I. Williams, Michael Mozer
    Directional-Unit Boltzmann Machines. [Citation Graph (0, 0)][DBLP]
    NIPS, 1992, pp:172-179 [Conf]
  24. Craig Boutilier, Richard S. Zemel, Benjamin Marlin
    Active Collaborative Filtering. [Citation Graph (0, 0)][DBLP]
    UAI, 2003, pp:98-106 [Conf]
  25. Max Welling, Richard S. Zemel, Geoffrey E. Hinton
    Efficient Parametric Projection Pursuit Density Estimation. [Citation Graph (0, 0)][DBLP]
    UAI, 2003, pp:575-582 [Conf]
  26. Richard S. Zemel, Jonathan Pillow
    Encoding multiple orientations in a recurrent network. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2000, v:32, n:, pp:609-616 [Journal]
  27. David A. Ross, Richard S. Zemel
    Learning Parts-Based Representations of Data. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2006, v:7, n:, pp:2369-2397 [Journal]
  28. Peter Dayan, Geoffrey E. Hinton, Radford M. Neal, Richard S. Zemel
    The Helmholtz machine. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 1995, v:7, n:5, pp:889-904 [Journal]
  29. Richard S. Zemel, Peter Dayan, Alexandre Pouget
    Probabilistic Interpretation of Population Codes. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 1998, v:10, n:2, pp:403-430 [Journal]
  30. Richard S. Zemel, Michael Mozer
    Localist Attractor Networks. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2001, v:13, n:5, pp:1045-1064 [Journal]
  31. Quentin J. M. Huys, Richard S. Zemel, Rama Natarajan, Peter Dayan
    Fast Population Coding. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2007, v:19, n:2, pp:404-441 [Journal]
  32. Richard S. Zemel, Christopher K. I. Williams, Michael Mozer
    Lending direction to neural networks. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 1995, v:8, n:4, pp:503-512 [Journal]

  33. Latent topic random fields: Learning using a taxonomy of labels. [Citation Graph (, )][DBLP]

  34. Learning stick-figure models using nonparametric Bayesian priors over trees. [Citation Graph (, )][DBLP]

  35. Unsupervised Learning of Skeletons from Motion. [Citation Graph (, )][DBLP]

  36. BoltzRank: learning to maximize expected ranking gain. [Citation Graph (, )][DBLP]

  37. Learning Hybrid Models for Image Annotation with Partially Labeled Data. [Citation Graph (, )][DBLP]

  38. Generative versus discriminative training of RBMs for classification of fMRI images. [Citation Graph (, )][DBLP]

  39. Characterizing response behavior in multisensory perception with conflicting cues. [Citation Graph (, )][DBLP]

  40. Flexible Priors for Exemplar-based Clustering. [Citation Graph (, )][DBLP]

  41. Collaborative prediction and ranking with non-random missing data. [Citation Graph (, )][DBLP]

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