Richard Zemel

Professor
Department of Computer Science
University of Toronto

Recent Research Highlights

Research Interests

Selected Papers

Course Offerings

Students & Post Docs

Prospective Students

Affiliations

News

Brief Academic Bio

Curriculum Vitae

Contact Info

Visitor Info

Contact:
Department of Computer Science
University of Toronto
Office: Pratt Building 290C
Packages: 10 King's College Road, Rm. 3302
Toronto, Ontario, CANADA M5S 3G4
Phone: (416) 978-7497
Fax: (416) 978-1455
Email: zemel [at] cs [dot] toronto [dot] edu

Selected Papers

2021	Renjie Liao, Raquel Urtasun, Richard Zemel. "A PAC-Bayesian approach to generalization bounds for graph neural networks." ICLR 2021.

2021	Mengye Ren, Michael Iuzzolino, Michael Mozer, Richard Zemel. "Wandering within a world: Online contextualized few-shot learning." ICLR 2021.

2021	James Lucas, Mengye Ren, Irene Kameni, Toni Pitassi, Richard Zemel. "Theoretical bounds on estimation error for meta-learning." ICLR 2021.

2021	Jake Snell, Richard Zemel. "Bayesian few-shot classification with one-vs-each Polya-Gamma augmented Gaussian Processes." ICLR 2021.

2021	Zhewei Sun, Richard Zemel, Yang Xu. "A computational framework for slang generation." Transactions of the Association for Computational Linguistics, 9: 478-462.

2021	James Lucas, Juhan Bae, Michael Zhang, Stanislav Fort, Richard Zemel, Roger Grosse. "On monotonic linear interpolation of neural network parameters." ICML 2021.

2021	Eleni Triantafillou, Hugo Larochelle, Richard Zemel, Vincent Dumoulin. "Universal template for few-shot dataset generalization." ICML 2021.

2021	Alex Wang, Mengye Ren, Richard Zemel. "SketchEmbedNet: Learning novel concepts by imitating drawings." ICML 2021.

2021	Elliot Creager, Joern Jacobsen, Richard Zemel. "Environment inference for invariant learning." ICML 2021.

2020	Robert Geirhos, JÃ¶rn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, Felix Wichmann . "Shortcut learning in deep neural networks." Nature Machine Intelligence. 2.

2020	Ethan Fetaya, Joern-Henrik Jacobsen, Will Grathwohl, Richard Zemel. "Understanding the limitations of conditional generative models." ICLR 2020.

2020	Will Grathwohl, Jackson Wang, Jorn Jacobsen, David Duvenaud, Richard Zemel. "Cutting out the middle-man: Training and evaluating energy-based models without sampling." ICML 2020.

2020	Elliot Creager, David Madras, Toni Pitassi, Richard Zemel. "Causal modeling for fairness in dynamical systems." ICML 2020.

2020	Martin Mladenov, Elliot Creager, O Ben-Porat, Kevin Swersky, Richard Zemel, Craig Boutilier. "Optimizing long-term social welfare in recommender systems: A constrained matching approach.." ICML 2020.

2019	Seyed Kamyar Seyed Ghasemipour, Richard Zemel, Shane Gu. "A divergence minimization perspective on imitation learning methods." CoRL 2019 [Best Paper Award].

2019	Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William Hamilton, David Duvenaud, Raquel Urtasun, Richard Zemel. "Efficient graph generation with graph recurrent attention networks." NeurIPS 2019.

2019	Seyed Kamyar Seyed Ghasemipour, Shane Gu, Richard Zemel. "SMILe: Scalable meta inverse reinforcement learning through context-conditional policies." NeurIPS 2019.

2019	Mengye Ren, Renjie Liao, Ethan Fetaya, Richard Zemel. " Incremental few-shot learning with attention attractor networks." NeurIPS 2019.

2019	Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, Richard Zemel. "Understanding the origins of bias in word embedding." ICML.

2019	Marc Law, Renjie Liao, Jake Snell, Richard Zemel. "Lorentzian distance learning for hyperbolic representations." ICML. [web page]

2019	Elliot Creager, David Madras, Joern-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, Richard Zemel. "Flexibly fair representation learning by disentanglement." ICML. [web page]

2019	Marc Law, Jake Snell, Amir-massoud Farahmand, Raquel Urtasun, Richard Zemel. "Dimensionality reduction for representing the knowledge of probabilistic models." ICLR.

2019	James Lucas, Shengyang Sun, Richard Zemel, Roger Grosse. "Aggregated momentum: Stability through passive damping." ICLR. [web page]

2019	JÃ¶rn-Henrik Jacobsen, Jens Behrmann, Richard Zemel, Matthias Bethge. "Excessive invariance causes adversarial vulnerability." ICLR. [web page]

2019	Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard Zemel. "LanczosNet: Multi-scale deep graph convolutional networks." ICLR. [web page]

2019	David Madras, Elliot Creager, Toni Pitassi, Richard Zemel. "Fairness through causal awareness: Learning causal latent-variable models for biased data.." Conference on Fairness, Accountability and Transparencey (FAT).*

2018	Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William Byrd, Matthew Might, Raquel Urtasun, Richard Zemel.. "Neural guided constraint logic programming for program synthesis." NIPS. [pdf]

2018	David Madras, Toni Pitassi, Richard Zemel. "Predict responsibly: improving fairness and accuracy by learning to defer." NIPS. [pdf]

2018	Jack Klys, Jake Snell, Richard Zemel. "Learning latent subspaces in variational autoencoders." NIPS. [pdf]

2018	Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel. "Neural relational inference for interacting systems." ICML. [pdf]

2018	Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger Grosse, Richard Zemel. "Adversarial distillation of Bayesian neural network posteriors." ICML. [pdf]

2018	David Madras, Elliot Creager, Toniann Pitassi, Richard Zemel. "Learning adversarially fair and transferable representations." ICML. [pdf]

2018	Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Zachary Pitkow, Raquel Urtasun, Richard Zemel. "Reviving and improving recurrent back-propagation." ICML. [pdf]

2018	Amir Rosenfeld, Richard Zemel, John K. Tsotsos. "The elephant in the room." arxiv.

2017	Eleni Triantafillou, Richard Zemel, Raquel Urtasun. "Few-shot learning through an information retrieval lens." NIPS. [pdf]

2017	Yujia Li, Alexander Schwing, Kuan-Chieh Wang, Richard Zemel. "Dualing GANs." NIPS. [pdf]

2017	Christos Louizos, Uri Shalit, Joris Mooij, David Sontag, Richard Zemel, Max Welling. "Causal effect inference with deep latent-variable models." NIPS. [pdf]

2017	Marc Law, Raquel Urtasun, Richard Zemel. "Deep spectral clustering learning." ICML. [pdf, web page]

2017	Marc Law, Yaoling Yu, Raquel Urtasun, Richard Zemel, Eric Xing. "Efficient multiple Instance metric learning using weakly supervised data." CVPR. [pdf]

2017	Jake Snell, Kevin Swersky, Richard Zemel. "Prototypical networks for few-shot learning." NIPS. [pdf, pdf]

2017	Jake Snell, Richard Zemel. "Stochastic segmentation trees." UAI. [pdf, pdf]

2017	Jake Snell, Karl Ridgeway, Renjie Liao, Brett Roads, Michael C. Mozer & Richard S. Zemel. "Learning to generate images with perceptual similarity metrics." ICIP. 11. 11. [pdf, pdf]

2017	Mengye Ren, Renjie Liao, Raquel Urtasun, Fabian H. Sinz, Richard S. Zemel. "Normalizing the normalizers: Comparing and extending network normalization schemes." ICLR 2017. [web page]

2017	Mengye Ren and Richard Zemel. "End-to-end instance segmentation with recurrent attention." CVPR. [pdf, code]

2016	Eleni Triantafillou, Jamie Ryan Kiros, Raquel Urtasun, Richard Zemel. " Towards generalizable sentence embeddings ." ACL Workshop on Representation Learning for NLP. [pdf]

2016	Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, Richard Zemel. "The variational fair autoencoder." ICLR 2016. [pdf]

2016	Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel. "Gated graph sequence neural networks." ICLR 2016. [pdf]

2016	Yang Song, Alex Schwing, Richard Zemel, Raquel Urtasun. "Training deep neural networks via direct loss minimization." ICML 2016. [pdf]

2016	Kuan-Chieh Wang, Richard Zemel. "Classifying NBA offensive plays using neural networks." Sloan Sports Analytics Conference. [pdf]

2016	Wenjie Luo, Yujia Li, Raquel Urtasun, Richard Zemel. "Understanding the effective receptive field in deep convolutional neural networks." NIPS 2016. [pdf, poster]

2016	Renjie Liao, Alexander Schwing, Richard Zemel, Raquel Urtasun. "Learning deep parsimonious representations." NIPS 2016. [pdf, poster]

2015	Ryan Kiros, Ruslan Salakhutdinov, Richard Zemel. "Unifying visual-semantic embeddings with multimodal neural language models." TACL, To Appear. [pdf, web page, code]

2015	Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Antonio Torralba, Raquel Urtasun, Sanja Fidler. "Skip-thought vectors ." NIPS 2015.. [pdf, code]

2015	Mengye Ren, Ryan Kiros, Richard Zemel. "Image question answering: A visual semantic embedding model and a new dataset ." NIPS 2015.. [pdf, code, notes]

2015	Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler. "Aligning books and movies: Towards story-like visual explanations by watching movies and reading books ." ICCV 2015. [pdf, web page]

2015	Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, Yoshua Bengio. "Show, attend and tell: Neural image caption generation with visual attention." ICML-2015: The 32nd International Conference on Machine Learning . [pdf, web page, notes, notes]

2015	Yujia Li, Kevin Swersky and Richard Zemel . "Generative moment matching networks." ICML-2015: The 32nd International Conference on Machine Learning. [pdf]

2015	Gregory Koch, Richard Zemel, Ruslan Salakhutdinov. " Siamese neural networks for one-shot image recognition." ICML 2015 Deep Learning Workshop. [pdf]

2014	Jasper Snoek, Kevin Swersky, Richard Zemel, Ryan Adams. "Input warping for Bayesian optimization of non-stationary functions." ICML-2014: The 31st International Conference on Machine Learning. [pdf]

2014	Ryan Kiros, Ruslan Salakhutdinov, Richard Zemel. "Multimodal neural language models." ICML-2014: The 31st International Conference on Machine Learning. [pdf]

2014	Yujia Li, Richard Zemel. "High order regularization for semi-supervised learning of structured output problems." ICML-2014: The 31st International Conference on Machine Learning. [pdf, poster, notes]

2014	Yujia Li, Richard Zemel. "Mean field networks." ICML Workshop on Learning Tractable Probabilistic Models. [pdf, notes]

2014	Laurent Charlin, Richard Zemel, Hugo Larochelle. "Leveraging user libraries to bootstrap collaborative filtering." KDD 2014: 20th ACM Conference on Knowledge Discovery and Data Mining. [pdf]

2014	Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov.. "A multiplicative model for learning distributed text-based attribute representations." NIPS-2014. [pdf, .zip]

2014	Yin Zheng, Richard Zemel, Yu-Jin Zhang and Hugo Larochelle. "A neural autoregressive approach to attention-based recognition." International Journal of Computer Vision, Special Issue on Deep Learning. [pdf]

2013	Yujia Li, Daniel Tarlow, Richard Zemel. "Exploring compositional high order pattern potentials for structured output learning." CVPR-2013: The 26th IEEE Conference on Computer Vision and Pattern Recognition. Oral Presentation. [pdf, poster, notes]

2013	Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, Richard Zemel. . "Stochastic k-neighborhood selection for supervised and unsupervised learning." ICML-2013: The 30th International Conference on Machine Learning. [pdf, notes]

2013	Richard Zemel, Yu (Ledell) Wu, Kevin Swersky, Toni Pitassi, Cynthia Dwork. "Learning fair representations." ICML-2013: The 30th International Conference on Machine Learning. [pdf, notes]

2013	Jasper Snoek, Ryan Adams, Richard Zemel. "Determinantal Point Process latent variable models for neural spiking data." NIPS-2013: Advances in Neural Information Processing Systems.. [pdf]

2013	Maks Volkovs and Richard Zemel. "Supervised CRF framework for preference aggregation." CIKM-2013: International Conference on Information and Knowledge Management. [pdf, code]

2013	Laurent Charlin and Richard Zemel. "The Toronto Paper Matching System: An automated paper-reviewer assignment system." ICML Workshop on Peer Reviewing and Publishing Models . [pdf]

2013	Ke Li, Kevin Swersky, Richard Zemel. "Efficient feature learning using Perturb-and-MAP." NIPS Workshop on Perturbations, Optimization, and Statistics. [pdf]

2013	James Martens, Arkadev Chattopadhyay, Toniann Pitassi, Richard Zemel. "On the representational efficiency of Restricted Boltzmann Machines." NIPS-2013: Advances in Neural Information Processing Systems. [pdf]

2012	Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, Richard Zemel. "Fairness through awareness." Proceedings of Innovations of Theoretical Computer Science. [pdf]

2012	Daniel Tarlow, Ryan Adams, and Richard Zemel. "Randomized optimum models for structured prediction." AIStats-2012: Fifteenth International Conference on Artificial Intelligence and Statistics. [pdf]

2012	Daniel Tarlow and Richard Zemel. "Structured output learning with high order loss functions." AIStats-2012: Fifteenth International Conference on Artificial Intelligence and Statistics. [pdf]

2012	Maksims Volkovs and Richard Zemel. "A flexible generative model for preference aggregation." WWW 2012: 21st International World Wide Web Conference. [pdf]

2012	Laurent Charlin, Richard Zemel, and Craig Boutilier. "Active learning for matching problems." ICML-2012: Proceedings of the 29th International Conference on Machine Learning. [pdf]

2012	Daniel Tarlow, Kevin Swersky, Richard Zemel, Ryan Adams, and Brendan Frey. "Fast exact inference for recursive cardinality models." UAI-2012: The 28th Conference on Uncertainty in Artificial Intelligence. [pdf]

2012	Kevin Swersky, Daniel Tarlow, Ryan Adams, Richard Zemel,& Brendan Frey. "Probabilistic n-choose-k models for classification and ranking." NIPS-2012: Advances in Neural Information Processing Systems. [pdf, poster, notes]

2012	Maksims Volkovs & Richard Zemel. "Collaborative ranking with 17 parameters." NIPS 2012: Advances in Neural Information Processing Systems. [pdf]

2012	Maksims Volkovs & Richard Zemel. "Efficient sampling for bipartite matching problems." NIPS-2012: Advances in Neural Information Processing Systems. [pdf, notes]

2012	Kevin Swersky, Daniel Tarlow, Ilya Sutskever, Richard Zemel, Ruslan Salakhutdinov, & Ryan Adams. "Cardinality restricted Boltzmann machines ." NIPS-2012: Advances in Neural Information Processing Systems.. [pdf, poster]

2012	Maksims Volkovs, Hugo Larochelle, and Richard Zemel. "Learning to rank by aggregating expert preferences." CIKM-2012: International Conference on Information and Knowledge Management. [pdf]

2011	Benjamin Marlin, Richard Zemel, Sam Roweis, & Malcolm Slaney. "Recommender systems: Missing data and statistical model estimation." IJCAI: 22nd International Joint Conference on Artificial Intelligence. [pdf]

2011	Laurent Charlin, Richard Zemel, & Craig Boutilier. "A framework for optimizing paper matching." UAI 2011: 27th Conference on Uncertainty in Artificial Intelligence. [pdf]

2011	Daniel Tarlow, Inmar Givoni, Richard Zemel, & Brendan Frey. "Graph cuts is a max-product algorithm." UAI 2011: 27th Conference on Uncertainty in Artificial Intelligence. [pdf]

2011	Rama Natarajan and Richard Zemel. "Dynamic cue combination in distributional population code networks." Sensory Cue Integration, Oxford University Press. Julia Trommersheuser, Konrad Kording, and Michael S. Landy (Eds.). [pdf]

2010	David Ross, Daniel Tarlow, and Richard Zemel. "Learning articulated structure and motion." International Journal on Computer Vision. 88(2):214-237. [pdf, video, web page]

2010	Daniel Tarlow, Inmar Givoni, and Richard Zemel. "HOP-MAP: Efficient message-passing with Higher-Order Potentials." AISTATS-2010: The 13th International Conference on Artificial Intelligence and Statistics. [pdf, code, poster]

2009	Ben Marlin and Richard Zemel. "Collaborative prediction and ranking with non-random missing data." Recsys-2009: ACM Conference on Recommender Systems. (Best technical paper). [pdf]

2009	Maksims Volkovs and Richard Zemel. "BoltzRank: Learning to maximize expected ranking gain." ICML-2009: International Conference on Machine Learning. (Best student paper). [pdf]

2008	Rama Natarajan, Iain Murray, Ladan Shams, and Richard Zemel. "Characterizing response behavior in multi-sensory perception with conflicting cues." NIPS-2008: Advances in Neural Information Processing Systems. [pdf]

2008	Xuming He and Richard Zemel. "Learning hybrid models for image annotation with partially labeled data." NIPS-2008: Advances in Neural Information Processing Systems. [pdf]

2008	Tanya Schmah, Geoffrey Hinton, Richard Zemel, Steven Small and Stephen Strother. "Generative versus discriminative training of RBMs for classification of fMRI images." NIPS-2008: Advances in Neural Information Processing Systems. [pdf]

2008	David Ross, Daniel Tarlow, and Richard Zemel. "Unsupervised learning of skeletons from motion." ECCV-2008: European Conference on Computer Vision. [pdf]

2008	Daniel Tarlow, Richard Zemel, and Brendan Frey. "Flexible priors for exemplar-based clustering." UAI-2008: The 24th Conference on Uncertainty in Artificial Intelligence. [pdf]

2008	Xuming He and Richard Zemel. "Latent topic random fields: Learning using a taxonomy of labels." CVPR-2008: IEEE Conference on Computer Vision and Pattern Recognition. [pdf]

2008	Ted Meeds, David Ross, Richard Zemel, and Sam Roweis. "Learning stick-figure models using nonparametric Bayesian priors over trees." CVPR-2008: IEEE Conference on Computer Vision and Pattern Recognition. [pdf]

2008	Rama Natarajan, Quentin Huys, Peter Dayan, and Richard Zemel. "Encoding and decoding spikes for dynamic stimuli." Neural Computation, 20(9): 2325-2360. [pdf]

2008	Liam Stewart, Xuming He, and Richard Zemel. "Learning flexible features for conditional random fields." IEEE Transactions on Pattern Analysis and Machine Intelligence. 30(8): 1415-1426. (preprint). [pdf]

2008	Francois Klam, Richard Zemel, and Alex Pouget. "Population coding with motion energy filters: The impact of correlations." Neural Computation. 20(1): 146-175. (reprint). [pdf]

2007	Ben Marlin, Richard Zemel, Sam Roweis and Malcolm Slaney. "Collaborative filtering and the missing at random assumption." UAI-2007: The 23rd Conference on Uncertainty in Artificial Intelligence. [pdf]

2007	David Ross, Daniel Tarlow and Richard Zemel. "Learning articulated skeletons from motion." Workshop on Dynamical Vision at ICCV-2007. [pdf]

2007	Quentin Huys, Richard Zemel, Rama Natarajan, and Peter Dayan. "Fast population coding." Neural Computation. 19(2): 460-97. (reprint). [pdf]

2006	Xuming He, Richard Zemel, and Volodymyr Mnih. "Topological map learning from outdoor image sequences." Journal of Field Robotics.. 23:1091-1104. (preprint). [pdf]

2006	Xuming He, Richard Zemel, and Deb Ray. "Learning and incorporating top-down cues in image segmentation." ECCV-2006: 9th European Conference on Computer Vision. [pdf]

2006	Jasper Snoek, Jesse Hoey, Liam Stewart, and Richard Zemel. "Automated detection of unusual events on stairs." Computer and Robot Vision 2006. [pdf]

2006	David Ross, Simon Osindero, and Richard Zemel. "Combining discriminative features to infer complex trajectories." ICML-2006: Twenty-Third International Conference on Machine Learning. [pdf, web page]

2006	David Ross and Richard Zemel. "Learning parts-based representations of data." Journal of Machine Learning Research. 7(Nov): 2369-2397. (preprint). [pdf, web page]

2005	Ben Marlin, Sam Roweis, and Richard Zemel. "Unsupervised learning with non-ignorable missing data." AISTATS. [pdf]

2005	Ben Marlin, Sam Roweis, and Richard Zemel. "Unsupervised learning with non-ignorable missing data." AISTATS-2005.

2004	Miguel Carreira-Perpinan and Richard Zemel. "Proximity graphs for clustering and manifold learning." NIPS-17: Advances in Neural Information Processing Systems 17.

2004	Ben Marlin and Richard Zemel. "The multiple multiplicative factor model for collaborative filtering." ICML-2004: Proceedings of the 21st International Conference on Machine Learning. (8 pages). [pdf]

2004	Richard Zemel, Quentin Huys, Rama Natarajan, and Peter Dayan. "Probabilistic computation in spiking populations." NIPS-17: Advances in Neural Information Processing Systems 17. (8 pages). [pdf]

2004	Miguel Carreira-Perpinan and Richard Zemel. "Proximity graphs for clustering and manifold learning." NIPS-17: Advances in Neural Information Processing Systems 17. (8 pages). [pdf]

2004	Xuming He, Richard Zemel, and Miguel Carreira-Perpinan. "Multiscale conditional random fields for image labelling." CVPR-2004: IEEE Conference on Computer Vision and Pattern Recognition. (8 pages). [pdf, ps.gz]

2004	Max Welling, Richard Zemel, and Geoffrey Hinton. "Probabilistic sequential independent components analysis." IEEE Transactions in Neural Networks, Special Issue on Information Theory. 15(4): 838-849. [pdf]

2003	Craig Boutilier, Richard Zemel, and Benjamin Marlin. "Active collaborative filtering." UAI-2003: 19th Conference on Uncertainty in Artificial Intelligence. (9 pages). [pdf, ps.gz]

2003	Max Welling, Richard Zemel, and Geoffrey Hinton. "Efficient parametric projection pursuit density estimation." UAI-2003: 19th Conference on Uncertainty in Artificial Intelligence. (8 pages). [ps.gz]

2003	Alex Pouget, Peter Dayan and Richard Zemel. "Computation and inference with population codes." UAI-2003: 19th Conference on Uncertainty in Artificial Intelligence. pp. 381-410. (33 pages). [pdf]

2002	David Ross and Richard Zemel. "Multiple-cause vector quantization." NIPS-15: Advances in Neural Information Processing Systems 15. (8 pages). [pdf, ps.gz]

2002	Max Welling, Richard Zemel, and Geoffrey Hinton. "Self supervised boosting." NIPS-15: Advances in Neural Information Processing Systems 15. (8 pages). [pdf, ps.gz]

2002	Craig Boutilier and Richard Zemel. "Online queries for collaborative filtering." Ninth International Workshop on Artificial Intelligence and Statistics. (8 pages). [pdf, ps.gz]

2002	Richard Zemel and Michael Mozer. "Localist attractor networks." Neural Computation. 13(5): 1045-1064. (20 pages). [pdf, ps.gz]

2002	Richard Zemel, Marlene Behrmann, Michael Mozer, and Daphne Bevalier. "Experience-dependent perceptual grouping and object-based attention." Journal of Experimental Psychology: Human Perception and Performance. 28(1): 202-217. (16 pages). [pdf, ps.gz]

2001	Richard Zemel and Toni Pitassi. "A gradient-based boosting algorithm for regression problems." NIPS-13: Advances in Neural Information Processing Systems 13. (7 pages). [ps.gz]

2000	Marlene Behrmann, Richard Zemel, and Michael Mozer. "Occlusion, symmetry, and object-based attention: Reply to Saiki." Journal of Experimental Psychology: Human Perception and Performance. 26(4): pp. 1497-1505.

2000	Zhiyong Yang and Richard Zemel. "Managing uncertainty in cue combination." NIPS-12: Advances in Neural Information Processing Systems 12. pp. 80-86. (7 pages). [ps.gz]

2000	Richard Zemel and Jon Pillow. "Encoding multiple orientations in a recurrent network." Neurocomputing. 32-33: 609-616. [pdf]

2000	Alex Pouget, Peter Dayan and Richard Zemel. "Information processing with population codes." Nature Reviews Neuroscience. 1: 125-132. pp. 125-132. (8 pages). [pdf]

2000	Richard Zemel. "Cortical belief networks." R. Hecht-Neilsen (Ed.): Theories of the Cerebral Cortex. (16 pages). [ps.gz]

1999	Richard Zemel and Peter Dayan. "Distributional population codes and multiple motion models." NIPS-11: Advances in Neural Information Processing Systems 11. pp. 174-180. [ps.gz]

1998	Richard Zemel, Peter Dayan, and Alex Pouget. "Probabilistic interpretation of population codes." Neural Computation. 10(2), pp. 403-430. 28 pages. [pdf, ps.gz]

1998	Richard Zemel and Terry Sejnowski. "A model for encoding multiple object motions and self-motion in area MST of primate visual cortex." The Journal of Neuroscience. 18(1), pp. 531-547. [pdf]

1998	Marlene Behrmann, Richard Zemel, and Michael Mozer. "Object-based attention and occlusion: Evidence from normal subjects and a computational model." Journal of Experimental Psychology: Human Perception and Performance. 24(4), pp. 1011-1036.

1997	Richard Zemel and Peter Dayan. "Combining probabilistic population codes." International Joint Conference on Artificial Intelligence 1997. (6 pages). [ps.gz]

1997	Peter Dayan and Richard Zemel. "Competition and multiple cause models." Neural Computation. 7(3), pp. 565-579. (15 pages). [ps.gz]

1997	Geoffrey Hinton and Richard Zemel. "Minimizing description length in an unsupervised neural network." Geoffrey Hinton and Richard Zemel. [ps.gz]

1995	Peter Dayan, Geoffrey Hinton, Radford Neal, and Richard Zemel. "The Helmholtz Machine." Neural Computation. 7(5), pp. 889-904. pp. 889-905.

1995	Richard Zemel, Chris Williams, and Michael Mozer. "Lending direction to neural networks." Neural Networks. 8(4), pp. 503-512. pp. 503-512. (20 pages). [ps.gz]

1995	Richard Zemel and Geoffrey Hinton. "Developing population codes by minimizing description length." Neural Computation. 7(3), pp. 549-564. pp. 549-564. (14 pages). [ps.gz]

1994	Richard Zemel. "A Minimum Description Length Framework for Unsupervised Learning." Ph.D. Thesis. Technical Report CRG-TR-93-2, University of Toronto: In 3 separate sections. [ps.gz, ps.gz, ps.gz]

1992	Michael Mozer, Richard Zemel, Marlene Behrmann, and Chris Williams. "Learning to segment images using dynamic feature binding." Neural Computation. 4(5), pp. 647-663.