2009 |
42 | EE | Jasper Snoek,
Jesse Hoey,
Liam Stewart,
Richard S. Zemel,
Alex Mihailidis:
Automated detection of unusual events on stairs.
Image Vision Comput. 27(1-2): 153-166 (2009) |
2008 |
41 | EE | Xuming He,
Richard S. Zemel:
Latent topic random fields: Learning using a taxonomy of labels.
CVPR 2008 |
40 | EE | Edward Meeds,
David A. Ross,
Richard S. Zemel,
Sam T. Roweis:
Learning stick-figure models using nonparametric Bayesian priors over trees.
CVPR 2008 |
39 | EE | David A. Ross,
Daniel Tarlow,
Richard S. Zemel:
Unsupervised Learning of Skeletons from Motion.
ECCV (3) 2008: 560-573 |
38 | EE | Rama Natarajan,
Iain Murray,
Ladan Shams,
Richard S. Zemel:
Characterizing response behavior in multisensory perception with conflicting cues.
NIPS 2008: 1153-1160 |
37 | EE | Xuming He,
Richard S. Zemel:
Learning Hybrid Models for Image Annotation with Partially Labeled Data.
NIPS 2008: 625-632 |
36 | EE | Daniel Tarlow,
Richard S. Zemel,
Brendan J. Frey:
Flexible Priors for Exemplar-based Clustering.
UAI 2008: 537-545 |
35 | EE | F. Klam,
Richard S. Zemel,
Alexandre Pouget:
Population Coding with Motion Energy Filters: The Impact of Correlations.
Neural Computation 20(1): 146-175 (2008) |
34 | EE | Rama Natarajan,
Quentin J. M. Huys,
Peter Dayan,
Richard S. Zemel:
Encoding and Decoding Spikes for Dynamic Stimuli.
Neural Computation 20(9): 2325-2360 (2008) |
2007 |
33 | EE | Quentin J. M. Huys,
Richard S. Zemel,
Rama Natarajan,
Peter Dayan:
Fast Population Coding.
Neural Computation 19(2): 404-441 (2007) |
2006 |
32 | EE | Jasper Snoek,
Jesse Hoey,
Liam Stewart,
Richard S. Zemel:
Automated Detection of Unusual Events on Stairs.
CRV 2006: 5 |
31 | EE | Xuming He,
Richard S. Zemel,
Debajyoti Ray:
Learning and Incorporating Top-Down Cues in Image Segmentation.
ECCV (1) 2006: 338-351 |
30 | EE | David A. Ross,
Simon Osindero,
Richard S. Zemel:
Combining discriminative features to infer complex trajectories.
ICML 2006: 761-768 |
29 | EE | Xuming He,
Richard S. Zemel,
Volodymyr Mnih:
Topological map learning from outdoor image sequences.
J. Field Robotics 23(11-12): 1091-1104 (2006) |
28 | EE | David A. Ross,
Richard S. Zemel:
Learning Parts-Based Representations of Data.
Journal of Machine Learning Research 7: 2369-2397 (2006) |
2004 |
27 | EE | Xuming He,
Richard S. Zemel,
Miguel Á. Carreira-Perpiñán:
Multiscale Conditional Random Fields for Image Labeling.
CVPR (2) 2004: 695-702 |
26 | EE | Benjamin Marlin,
Richard S. Zemel:
The multiple multiplicative factor model for collaborative filtering.
ICML 2004 |
25 | EE | Richard S. Zemel,
Quentin J. M. Huys,
Rama Natarajan,
Peter Dayan:
Probabilistic Computation in Spiking Populations.
NIPS 2004 |
24 | EE | Miguel Á. Carreira-Perpiñán,
Richard S. Zemel:
Proximity Graphs for Clustering and Manifold Learning.
NIPS 2004 |
2003 |
23 | | Max Welling,
Richard S. Zemel,
Geoffrey E. Hinton:
Efficient Parametric Projection Pursuit Density Estimation.
UAI 2003: 575-582 |
22 | | Craig Boutilier,
Richard S. Zemel,
Benjamin Marlin:
Active Collaborative Filtering.
UAI 2003: 98-106 |
2002 |
21 | EE | David A. Ross,
Richard S. Zemel:
Multiple Cause Vector Quantization.
NIPS 2002: 1017-1024 |
20 | EE | Max Welling,
Richard S. Zemel,
Geoffrey E. Hinton:
Self Supervised Boosting.
NIPS 2002: 665-672 |
2001 |
19 | | Richard S. Zemel,
Michael Mozer:
Localist Attractor Networks.
Neural Computation 13(5): 1045-1064 (2001) |
2000 |
18 | | Richard S. Zemel,
Toniann Pitassi:
A Gradient-Based Boosting Algorithm for Regression Problems.
NIPS 2000: 696-702 |
17 | EE | Richard S. Zemel,
Jonathan Pillow:
Encoding multiple orientations in a recurrent network.
Neurocomputing 32-33: 609-616 (2000) |
1999 |
16 | EE | Richard S. Zemel,
Michael Mozer:
A Generative Model for Attractor Dynamics.
NIPS 1999: 80-88 |
15 | EE | Zhiyong Yang,
Richard S. Zemel:
Managing Uncertainty in Cue Combination.
NIPS 1999: 869-878 |
1998 |
14 | EE | Richard S. Zemel,
Peter Dayan:
Distributional Population Codes and Multiple Motion Models.
NIPS 1998: 174-182 |
13 | | Richard S. Zemel,
Peter Dayan,
Alexandre Pouget:
Probabilistic Interpretation of Population Codes.
Neural Computation 10(2): 403-430 (1998) |
1997 |
12 | | Richard S. Zemel,
Peter Dayan:
Combining Probabilistic Population Codes.
IJCAI 1997: 1114-1119 |
1996 |
11 | EE | Richard S. Zemel,
Peter Dayan,
Alexandre Pouget:
Probabilistic Interpretation of Population Codes.
NIPS 1996: 676-684 |
10 | EE | Michael S. Gray,
Alexandre Pouget,
Richard S. Zemel,
Steven J. Nowlan,
Terrence J. Sejnowski:
Selective Integration: A Model for Disparity Estimation.
NIPS 1996: 866-872 |
1995 |
9 | EE | Peter Dayan,
Geoffrey E. Hinton,
Radford M. Neal,
Richard S. Zemel:
The Helmholtz machine.
Neural Computation 7(5): 889-904 (1995) |
8 | EE | Richard S. Zemel,
Christopher K. I. Williams,
Michael Mozer:
Lending direction to neural networks.
Neural Networks 8(4): 503-512 (1995) |
1994 |
7 | EE | Richard S. Zemel,
Terrence J. Sejnowski:
Grouping Components of Three-Dimensional Moving Objects in Area MST of Visual Cortex.
NIPS 1994: 165-172 |
1993 |
6 | EE | Richard S. Zemel,
Geoffrey E. Hinton:
Developing Population Codes by Minimizing Description Length.
NIPS 1993: 11-18 |
5 | EE | Geoffrey E. Hinton,
Richard S. Zemel:
Autoencoders, Minimum Description Length and Helmholtz Free Energy.
NIPS 1993: 3-10 |
1992 |
4 | EE | Richard S. Zemel,
Christopher K. I. Williams,
Michael Mozer:
Directional-Unit Boltzmann Machines.
NIPS 1992: 172-179 |
1991 |
3 | EE | Michael Mozer,
Richard S. Zemel,
Marlene Behrmann:
Learning to Segment Images Using Dynamic Feature Binding.
NIPS 1991: 436-443 |
1990 |
2 | EE | Richard S. Zemel,
Geoffrey E. Hinton:
Discovering Viewpoint-Invariant Relationships That Characterize Objects.
NIPS 1990: 299-305 |
1989 |
1 | EE | Richard S. Zemel,
Michael Mozer,
Geoffrey E. Hinton:
TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations.
NIPS 1989: 266-273 |