| Gated Graph Sequence Neural Networks Y Li, D Tarlow, M Brockschmidt, R Zemel International Conference on Learning Representations (ICLR 2016)., 2015 | 2765 | 2015 |
| DeepCoder: Learning to Write Programs M Balog, AL Gaunt, M Brockschmidt, S Nowozin, D Tarlow International Conference on Learning Representations (ICLR 2017)., 2016 | 532 | 2016 |
| A* Sampling CJ Maddison, D Tarlow, T Minka Advances in Neural Information Processing Systems, 3086-3094, 2014 | 309 | 2014 |
| Perturbations, Optimization, and Statistics T Hazan, G Papandreou, D Tarlow MIT Press, 2016 | 197* | 2016 |
| Bimodal modelling of source code and natural language M Allamanis, D Tarlow, A Gordon, Y Wei Proceedings of The 32nd International Conference on Machine Learning, 2123-2132, 2015 | 197 | 2015 |
| Structured generative models of natural source code C Maddison, D Tarlow Proceedings of the 31st International Conference on Machine Learning (ICML …, 2014 | 170 | 2014 |
| HOP-MAP: Efficient message passing with high order potentials D Tarlow, IE Givoni, RS Zemel Proceedings of 13th Conference on Artificial Intelligence and Statistics, 2010 | 165 | 2010 |
| TerpreT: A Probabilistic Programming Language for Program Induction AL Gaunt, M Brockschmidt, R Singh, N Kushman, P Kohli, J Taylor, ... arXiv preprint arXiv:1608.04428, 2016 | 117 | 2016 |
| Fits Like a Glove: Rapid and Reliable Hand Shape Personalization DJ Tan, T Cashman, J Taylor, A Fitzgibbon, D Tarlow, S Khamis, S Izadi, ... IEEE Conference on Computer Vision and Pattern Recognition, 2016 | 109 | 2016 |
| Using combinatorial optimization within max-product belief propagation J Duchi, D Tarlow, G Elidan, D Koller Advances in neural information processing systems 19, 369, 2007 | 96* | 2007 |
| Structured Denoising Diffusion Models in Discrete State-Spaces J Austin, D Johnson, J Ho, D Tarlow, R Berg NeurIPS 2021, 2021 | 73 | 2021 |
| Fast Exact Inference for Recursive Cardinality Models D Tarlow, K Swersky, RS Zemel, RP Adams, BJ Frey Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, 2012 | 73 | 2012 |
| Learning articulated structure and motion DA Ross, D Tarlow, RS Zemel International journal of computer vision 88 (2), 214-237, 2010 | 69 | 2010 |
| Structured Output Learning with High Order Loss Functions D Tarlow, RS Zemel Proceedings of the 15th Conference on Artificial Intelligence and Statistics, 2012 | 68* | 2012 |
| Graph Partition Neural Networks for Semi-Supervised Classification R Liao, M Brockschmidt, D Tarlow, AL Gaunt, R Urtasun, R Zemel ICLR 2018 Workshop Track, 2018 | 65 | 2018 |
| Optimizing Expected Intersection-over-Union with Candidate-Constrained CRFs F Ahmed, D Tarlow, D Batra Proc. of the IEEE International Conf. on Computer Vision (ICCV) 8, 2015 | 61 | 2015 |
| Differentiable Programs with Neural Libraries AL Gaunt, M Brockschmidt, N Kushman, D Tarlow International Conference on Machine Learning, 1213-1222, 2017 | 58 | 2017 |
| Randomized optimum models for structured prediction D Tarlow, RP Adams, RS Zemel Proceedings of the 15th Conference on Artificial Intelligence and Statistics …, 2012 | 54 | 2012 |
| Learning to fix build errors with graph2diff neural networks D Tarlow, S Moitra, A Rice, Z Chen, PA Manzagol, C Sutton, E Aftandilian Proceedings of the IEEE/ACM 42nd International Conference on Software …, 2020 | 52 | 2020 |
| Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning D Tarlow, K Swersky, L Charlin, I Sutskever, RS Zemel Proceedings of the International Conference on Machine Learning (ICML), 2013 | 52 | 2013 |
Richard ZemelProfessor of Computer Science, University of TorontoVerified email at cs.toronto.edu
