Year |
Citation |
Score |
2016 |
Zhu Y, Kiros R, Zemel R, Salakhutdinov R, Urtasun R, Torralba A, Fidler S. Aligning books and movies: Towards story-like visual explanations by watching movies and reading books Proceedings of the Ieee International Conference On Computer Vision. 11: 19-27. DOI: 10.1109/ICCV.2015.11 |
0.533 |
|
2015 |
Lake BM, Salakhutdinov R, Tenenbaum JB. Human-level concept learning through probabilistic program induction. Science (New York, N.Y.). 350: 1332-8. PMID 26659050 DOI: 10.1126/science.aab3050 |
0.349 |
|
2014 |
Plis SM, Hjelm DR, Salakhutdinov R, Allen EA, Bockholt HJ, Long JD, Johnson HJ, Paulsen JS, Turner JA, Calhoun VD. Deep learning for neuroimaging: a validation study. Frontiers in Neuroscience. 8: 229. PMID 25191215 DOI: 10.3389/Fnins.2014.00229 |
0.366 |
|
2014 |
Hjelm RD, Calhoun VD, Salakhutdinov R, Allen EA, Adali T, Plis SM. Restricted Boltzmann machines for neuroimaging: an application in identifying intrinsic networks. Neuroimage. 96: 245-60. PMID 24680869 DOI: 10.1016/J.Neuroimage.2014.03.048 |
0.342 |
|
2014 |
Kiros R, Zemel RS, Salakhutdinov R. A multiplicative model for learning distributed text-based attribute representations Advances in Neural Information Processing Systems. 3: 2348-2356. |
0.582 |
|
2014 |
Kiros R, Salakhutdinov R, Zemel R. Multimodal neural language models 31st International Conference On Machine Learning, Icml 2014. 3: 2012-2025. |
0.547 |
|
2014 |
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: A simple way to prevent neural networks from overfitting Journal of Machine Learning Research. 15: 1929-1958. |
0.455 |
|
2013 |
Bengio S, Deng L, Larochelle H, Lee H, Salakhutdinov R. Guest editors' introduction: special section on learning deep architectures. Ieee Transactions On Pattern Analysis and Machine Intelligence. 35: 1795-7. PMID 23946944 DOI: 10.1109/Tpami.2013.118 |
0.355 |
|
2013 |
Salakhutdinov R, Tenenbaum JB, Torralba A. Learning with hierarchical-deep models. Ieee Transactions On Pattern Analysis and Machine Intelligence. 35: 1958-71. PMID 23787346 DOI: 10.1109/Tpami.2012.269 |
0.358 |
|
2013 |
Srivastava N, Salakhutdinov R, Hinton G. Modeling documents with a Deep Boltzmann Machine Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, Uai 2013. 616-624. |
0.546 |
|
2013 |
Tang Y, Salakhutdinov R, Hinton G. Tensor analyzers 30th International Conference On Machine Learning, Icml 2013. 1200-1208. |
0.422 |
|
2012 |
Salakhutdinov R, Hinton G. An efficient learning procedure for deep Boltzmann machines. Neural Computation. 24: 1967-2006. PMID 22509963 DOI: 10.1162/NECO_a_00311 |
0.589 |
|
2012 |
Tang Y, Salakhutdinov R, Hinton G. Robust Boltzmann Machines for recognition and denoising Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition. 2264-2271. DOI: 10.1109/CVPR.2012.6247936 |
0.529 |
|
2012 |
Salakhutdinov R, Hinton G. A better way to pretrain Deep Boltzmann Machines Advances in Neural Information Processing Systems. 3: 2447-2455. |
0.535 |
|
2012 |
Tang Y, Salakhutdinov R, Hinton G. Deep Lambertian networks Proceedings of the 29th International Conference On Machine Learning, Icml 2012. 2: 1623-1630. |
0.447 |
|
2012 |
Tang Y, Salakhutdinov R, Hinton G. Deep mixtures of factor analysers Proceedings of the 29th International Conference On Machine Learning, Icml 2012. 1: 505-512. |
0.429 |
|
2012 |
Swersky K, Tarlow D, Sutskever I, Salakhutdinov R, Zemel RS, Adams RP. Cardinality restricted boltzmann machines Advances in Neural Information Processing Systems. 4: 3293-3301. |
0.653 |
|
2011 |
Hinton G, Salakhutdinov R. Discovering binary codes for documents by learning deep generative models. Topics in Cognitive Science. 3: 74-91. PMID 25164175 DOI: 10.1111/j.1756-8765.2010.01109.x |
0.551 |
|
2009 |
Salakhutdinov R, Hinton G. Semantic hashing International Journal of Approximate Reasoning. 50: 969-978. DOI: 10.1016/j.ijar.2008.11.006 |
0.532 |
|
2009 |
Salakhutdinov R, Hinton G. Deep Boltzmann machines Journal of Machine Learning Research. 5: 448-455. |
0.532 |
|
2009 |
Salakhutdinov R, Hinton G. Replicated softmax: An undirected topic model Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. 1607-1614. |
0.445 |
|
2009 |
Salakhutdinov R, Hinton G. Using deep belief nets to learn covariance kernels for Gaussian processes Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference. |
0.506 |
|
2007 |
Salakhutdinov R, Mnih A, Hinton G. Restricted Boltzmann machines for collaborative filtering Acm International Conference Proceeding Series. 227: 791-798. DOI: 10.1145/1273496.1273596 |
0.485 |
|
2007 |
Salakhutdinov R, Hinton G. Learning a nonlinear embedding by preserving class neighbourhood structure Journal of Machine Learning Research. 2: 412-419. |
0.479 |
|
2006 |
Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science (New York, N.Y.). 313: 504-7. PMID 16873662 DOI: 10.1126/science.1127647 |
0.485 |
|
2005 |
Roweis ST, Salakhutdinov RR. Simultaneous localization and surveying with multiple agents Lecture Notes in Computer Science. 3355: 313-332. |
0.547 |
|
2005 |
Goldberger J, Roweis S, Hinton G, Salakhutdinov R. Neighbourhood components analysis Advances in Neural Information Processing Systems. |
0.63 |
|
2003 |
Salakhutdinov R, Roweis S, Ghahramani Z. Optimization with EM and Expectation-Conjugate-Gradient Proceedings, Twentieth International Conference On Machine Learning. 2: 672-679. |
0.63 |
|
2003 |
Salakhutdinov R, Roweis S. Adaptive Overrelaxed Bound Optimization Methods Proceedings, Twentieth International Conference On Machine Learning. 2: 664-671. |
0.568 |
|
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