Richard S. Zemel

	2009
47		Maksims Volkovs, Richard S. Zemel: BoltzRank: learning to maximize expected ranking gain. ICML 2009: 137
46		Benjamin M. Marlin, Richard S. Zemel: Collaborative prediction and ranking with non-random missing data. RecSys 2009: 5-12
45		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
44		Xuming He, Richard S. Zemel: Latent topic random fields: Learning using a taxonomy of labels. CVPR 2008
43		Edward Meeds, David A. Ross, Richard S. Zemel, Sam T. Roweis: Learning stick-figure models using nonparametric Bayesian priors over trees. CVPR 2008
42		David A. Ross, Daniel Tarlow, Richard S. Zemel: Unsupervised Learning of Skeletons from Motion. ECCV (3) 2008: 560-573
41		Rama Natarajan, Iain Murray, Ladan Shams, Richard S. Zemel: Characterizing response behavior in multisensory perception with conflicting cues. NIPS 2008: 1153-1160
40		Tanya Schmah, Geoffrey E. Hinton, Richard S. Zemel, Steven L. Small, Stephen C. Strother: Generative versus discriminative training of RBMs for classification of fMRI images. NIPS 2008: 1409-1416
39		Xuming He, Richard S. Zemel: Learning Hybrid Models for Image Annotation with Partially Labeled Data. NIPS 2008: 625-632
38		Daniel Tarlow, Richard S. Zemel, Brendan J. Frey: Flexible Priors for Exemplar-based Clustering. UAI 2008: 537-545
37		Liam Stewart, Xuming He, Richard S. Zemel: Learning Flexible Features for Conditional Random Fields. IEEE Trans. Pattern Anal. Mach. Intell. 30(8): 1415-1426 (2008)
36		F. Klam, Richard S. Zemel, Alexandre Pouget: Population Coding with Motion Energy Filters: The Impact of Correlations. Neural Computation 20(1): 146-175 (2008)
35		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
34		Quentin J. M. Huys, Richard S. Zemel, Rama Natarajan, Peter Dayan: Fast Population Coding. Neural Computation 19(2): 404-441 (2007)
	2006
33		Jasper Snoek, Jesse Hoey, Liam Stewart, Richard S. Zemel: Automated Detection of Unusual Events on Stairs. CRV 2006: 5
32		Xuming He, Richard S. Zemel, Debajyoti Ray: Learning and Incorporating Top-Down Cues in Image Segmentation. ECCV (1) 2006: 338-351
31		David A. Ross, Simon Osindero, Richard S. Zemel: Combining discriminative features to infer complex trajectories. ICML 2006: 761-768
30		Xuming He, Richard S. Zemel, Volodymyr Mnih: Topological map learning from outdoor image sequences. J. Field Robotics 23(11-12): 1091-1104 (2006)
29		David A. Ross, Richard S. Zemel: Learning Parts-Based Representations of Data. Journal of Machine Learning Research 7: 2369-2397 (2006)
	2004
28		Xuming He, Richard S. Zemel, Miguel Á. Carreira-Perpiñán: Multiscale Conditional Random Fields for Image Labeling. CVPR (2) 2004: 695-702
27		Benjamin M. Marlin, Richard S. Zemel: The multiple multiplicative factor model for collaborative filtering. ICML 2004
26		Richard S. Zemel, Quentin J. M. Huys, Rama Natarajan, Peter Dayan: Probabilistic Computation in Spiking Populations. NIPS 2004
25		Miguel Á. Carreira-Perpiñán, Richard S. Zemel: Proximity Graphs for Clustering and Manifold Learning. NIPS 2004
	2003
24		Max Welling, Richard S. Zemel, Geoffrey E. Hinton: Efficient Parametric Projection Pursuit Density Estimation. UAI 2003: 575-582
23		Craig Boutilier, Richard S. Zemel, Benjamin M. Marlin: Active Collaborative Filtering. UAI 2003: 98-106
	2002
22		David A. Ross, Richard S. Zemel: Multiple Cause Vector Quantization. NIPS 2002: 1017-1024
21		Max Welling, Richard S. Zemel, Geoffrey E. Hinton: Self Supervised Boosting. NIPS 2002: 665-672
	2001
20		Richard S. Zemel, Michael Mozer: Localist Attractor Networks. Neural Computation 13(5): 1045-1064 (2001)
	2000
19		Richard S. Zemel, Toniann Pitassi: A Gradient-Based Boosting Algorithm for Regression Problems. NIPS 2000: 696-702
18		Richard S. Zemel, Jonathan Pillow: Encoding multiple orientations in a recurrent network. Neurocomputing 32-33: 609-616 (2000)
	1999
17		Richard S. Zemel, Michael Mozer: A Generative Model for Attractor Dynamics. NIPS 1999: 80-88
16		Zhiyong Yang, Richard S. Zemel: Managing Uncertainty in Cue Combination. NIPS 1999: 869-878
	1998
15		Richard S. Zemel, Peter Dayan: Distributional Population Codes and Multiple Motion Models. NIPS 1998: 174-182
14		Richard S. Zemel, Peter Dayan, Alexandre Pouget: Probabilistic Interpretation of Population Codes. Neural Computation 10(2): 403-430 (1998)
	1997
13		Richard S. Zemel, Peter Dayan: Combining Probabilistic Population Codes. IJCAI 1997: 1114-1119
	1996
12		Richard S. Zemel, Peter Dayan, Alexandre Pouget: Probabilistic Interpretation of Population Codes. NIPS 1996: 676-684
11		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
10		Peter Dayan, Geoffrey E. Hinton, Radford M. Neal, Richard S. Zemel: The Helmholtz machine. Neural Computation 7(5): 889-904 (1995)
9		Richard S. Zemel, Christopher K. I. Williams, Michael Mozer: Lending direction to neural networks. Neural Networks 8(4): 503-512 (1995)
	1994
8		Richard S. Zemel, Terrence J. Sejnowski: Grouping Components of Three-Dimensional Moving Objects in Area MST of Visual Cortex. NIPS 1994: 165-172
	1993
7		Richard S. Zemel, Geoffrey E. Hinton: Developing Population Codes by Minimizing Description Length. NIPS 1993: 11-18
6		Geoffrey E. Hinton, Richard S. Zemel: Autoencoders, Minimum Description Length and Helmholtz Free Energy. NIPS 1993: 3-10
	1992
5		Richard S. Zemel, Christopher K. I. Williams, Michael Mozer: Directional-Unit Boltzmann Machines. NIPS 1992: 172-179
4		Michael C. Mozer, Richard S. Zemel, Marlene Behrmann, Christopher K. I. Williams: Learning to Segment Images Using Dynamic Feature Binding. Neural Computation 4(5): 650-665 (1992)
	1991
3		Michael Mozer, Richard S. Zemel, Marlene Behrmann: Learning to Segment Images Using Dynamic Feature Binding. NIPS 1991: 436-443
	1990
2		Richard S. Zemel, Geoffrey E. Hinton: Discovering Viewpoint-Invariant Relationships That Characterize Objects. NIPS 1990: 299-305
	1989
1		Richard S. Zemel, Michael Mozer, Geoffrey E. Hinton: TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations. NIPS 1989: 266-273

Coauthor Index

1	Marlene Behrmann	[3] [4]
2	Craig Boutilier	[23]
3	Miguel Á. Carreira-Perpiñán	[25] [28]
4	Peter Dayan	[10] [12] [13] [14] [15] [26] [34] [35]
5	Brendan J. Frey	[38]
6	Michael S. Gray	[11]
7	Xuming He	[28] [30] [32] [37] [39] [44]
8	Geoffrey E. Hinton	[1] [2] [6] [7] [10] [21] [24] [40]
9	Jesse Hoey	[33] [45]
10	Quentin J. M. Huys	[26] [34] [35]
11	F. Klam	[36]
12	Benjamin M. Marlin	[23] [27] [46]
13	Edward Meeds	[43]
14	Alex Mihailidis	[45]
15	Volodymyr Mnih	[30]
16	Michael C. Mozer (Michael Mozer)	[1] [3] [4] [5] [9] [17] [20]
17	Iain Murray	[41]
18	Rama Natarajan	[26] [34] [35] [41]
19	Radford M. Neal	[10]
20	Steven J. Nowlan	[11]
21	Simon Osindero	[31]
22	Jonathan Pillow	[18]
23	Toniann Pitassi	[19]
24	Alexandre Pouget	[11] [12] [14] [36]
25	Debajyoti Ray	[32]
26	David A. Ross	[22] [29] [31] [42] [43]
27	Sam T. Roweis	[43]
28	Tanya Schmah	[40]
29	Terrence J. Sejnowski	[8] [11]
30	Ladan Shams	[41]
31	Steven L. Small	[40]
32	Jasper Snoek	[33] [45]
33	Liam Stewart	[33] [37] [45]
34	Stephen C. Strother	[40]
35	Daniel Tarlow	[38] [42]
36	Maksims Volkovs	[47]
37	Max Welling	[21] [24]
38	Christopher K. I. Williams	[4] [5] [9]
39	Zhiyong Yang	[16]

Colors in the list of coauthors