| Year |
Citation |
Score |
| 2020 |
Nazábal A, Olmos PM, Ghahramani Z, Valera I. Handling incomplete heterogeneous data using VAEs Pattern Recognition. 107: 107501. DOI: 10.1016/j.patcog.2020.107501 |
0.349 |
|
| 2019 |
Lomelí M, Rowland M, Gretton A, Ghahramani Z. Antithetic and Monte Carlo kernel estimators for partial rankings Statistics and Computing. 29: 1127-1147. DOI: 10.1007/s11222-019-09859-z |
0.351 |
|
| 2016 |
Hernández-Lobato JM, Gelbart MA, Adams RP, Hoffman MW, Ghahramani Z. A general framework for constrained Bayesian optimization using information-based search Journal of Machine Learning Research. 17: 5549-5601. DOI: 10.17863/Cam.6477 |
0.532 |
|
| 2015 |
Palla K, Knowles DA, Ghahramani Z. Relational Learning and Network Modelling Using Infinite Latent Attribute Models. Ieee Transactions On Pattern Analysis and Machine Intelligence. 37: 462-74. PMID 26353254 DOI: 10.1109/Tpami.2014.2324586 |
0.657 |
|
| 2015 |
Knowles DA, Ghahramani Z. Pitman Yor Diffusion Trees for Bayesian Hierarchical Clustering. Ieee Transactions On Pattern Analysis and Machine Intelligence. 37: 271-89. PMID 26353241 DOI: 10.1109/Tpami.2014.2313115 |
0.64 |
|
| 2015 |
Bousmalis K, Zafeiriou S, Morency LP, Pantic M, Ghahramani Z. Variational Infinite Hidden Conditional Random Fields. Ieee Transactions On Pattern Analysis and Machine Intelligence. 37: 1917-29. PMID 26353136 DOI: 10.1109/Tpami.2014.2388228 |
0.37 |
|
| 2015 |
Bratieres S, Quadrianto N, Ghahramani Z. GPstruct: Bayesian Structured Prediction Using Gaussian Processes. Ieee Transactions On Pattern Analysis and Machine Intelligence. 37: 1514-20. PMID 26352456 DOI: 10.1109/TPAMI.2014.2366151 |
0.342 |
|
| 2015 |
Ghahramani Z. Probabilistic machine learning and artificial intelligence Nature. 521: 452-459. PMID 26017444 DOI: 10.1038/nature14541 |
0.373 |
|
| 2015 |
Tarran B, Ghahramani Z. How machines learned to think statistically Significance. 12: 8-15. DOI: 10.1111/j.1740-9713.2015.00796.x |
0.317 |
|
| 2015 |
Palla K, Knowles DA, Ghahramani Z. Relational learning and network modelling using infinite latent attribute models Ieee Transactions On Pattern Analysis and Machine Intelligence. 37: 462-474. DOI: 10.1109/TPAMI.2014.2324586 |
0.638 |
|
| 2015 |
Knowles DA, Ghahramani Z. Pitman yor diffusion trees for bayesian hierarchical clustering Ieee Transactions On Pattern Analysis and Machine Intelligence. 37: 271-289. DOI: 10.1109/TPAMI.2014.2313115 |
0.539 |
|
| 2015 |
Ge H, Chen Y, Wan M, Ghahramani Z. Distributed inference for Dirichlet process mixture models 32nd International Conference On Machine Learning, Icml 2015. 3: 2266-2274. |
0.312 |
|
| 2015 |
Gal Y, Chen Y, Ghahramani Z. Latent Gaussian processes for distribution estimation of multivariate categorical data 32nd International Conference On Machine Learning, Icml 2015. 1: 645-654. |
0.305 |
|
| 2014 |
Duvenaud D, Rippel O, Adams RP, Ghahramani Z. Avoiding pathologies in very deep networks Journal of Machine Learning Research. 33: 202-210. |
0.442 |
|
| 2013 |
Darkins R, Cooke EJ, Ghahramani Z, Kirk PD, Wild DL, Savage RS. Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm. Plos One. 8: e59795. PMID 23565168 DOI: 10.1371/Journal.Pone.0059795 |
0.316 |
|
| 2013 |
Eaton F, Ghahramani Z. Model reductions for inference: generality of pairwise, binary, and planar factor graphs. Neural Computation. 25: 1213-60. PMID 23547951 DOI: 10.1162/NECO_a_00441 |
0.331 |
|
| 2013 |
Ghahramani Z. Bayesian non-parametrics and the probabilistic approach to modelling. Philosophical Transactions. Series a, Mathematical, Physical, and Engineering Sciences. 371: 20110553. PMID 23277609 DOI: 10.1098/rsta.2011.0553 |
0.418 |
|
| 2012 |
Kirk P, Griffin JE, Savage RS, Ghahramani Z, Wild DL. Bayesian correlated clustering to integrate multiple datasets. Bioinformatics (Oxford, England). 28: 3290-7. PMID 23047558 DOI: 10.1093/Bioinformatics/Bts595 |
0.35 |
|
| 2011 |
Knowles D, Ghahramani Z. Nonparametric Bayesian sparse factor models with application to gene expression modeling Annals of Applied Statistics. 5: 1534-1552. DOI: 10.1214/10-Aoas435 |
0.64 |
|
| 2011 |
Griffiths TL, Ghahramani Z. The Indian buffet process: An introduction and review Journal of Machine Learning Research. 12: 1185-1224. |
0.377 |
|
| 2011 |
Lacoste-Julien S, Huszár F, Ghahramani Z. Approximate inference for the loss-calibrated Bayesian Journal of Machine Learning Research. 15: 416-424. |
0.713 |
|
| 2011 |
Abbott JT, Heller KA, Ghahramani Z, Griffiths TL. Testing a Bayesian measure of representativeness using a large image database Advances in Neural Information Processing Systems 24: 25th Annual Conference On Neural Information Processing Systems 2011, Nips 2011. |
0.392 |
|
| 2010 |
Adams RP, Wallach HM, Ghahramani Z. Learning the structure of deep sparse graphical models Journal of Machine Learning Research. 9: 1-8. |
0.556 |
|
| 2010 |
Adams RP, Ghahramani Z, Jordan MI. Tree-structured stick breaking for hierarchical data Advances in Neural Information Processing Systems 23: 24th Annual Conference On Neural Information Processing Systems 2010, Nips 2010. |
0.571 |
|
| 2009 |
Rasmussen CE, de la Cruz BJ, Ghahramani Z, Wild DL. Modeling and visualizing uncertainty in gene expression clusters using dirichlet process mixtures. Ieee/Acm Transactions On Computational Biology and Bioinformatics / Ieee, Acm. 6: 615-28. PMID 19875860 DOI: 10.1109/Tcbb.2007.70269 |
0.7 |
|
| 2009 |
Savage RS, Heller K, Xu Y, Ghahramani Z, Truman WM, Grant M, Denby KJ, Wild DL. R/BHC: fast Bayesian hierarchical clustering for microarray data. Bmc Bioinformatics. 10: 242. PMID 19660130 DOI: 10.1186/1471-2105-10-242 |
0.311 |
|
| 2009 |
Adams RP, Ghahramani Z. Archipelago: Nonparametric Bayesian semi-supervised learning Proceedings of the 26th International Conference On Machine Learning, Icml 2009. 1-8. DOI: 10.1145/1553374.1553375 |
0.522 |
|
| 2009 |
Van Gael J, Teh YW, Ghahramani Z. The infinite factorial hidden Markov model Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 1697-1704. |
0.605 |
|
| 2009 |
Adams RP, Ghahramani Z. Archipelago: Nonparametric Bayesian semi-supervised learning Proceedings of the 26th International Conference On Machine Learning, Icml 2009. 1-8. |
0.522 |
|
| 2008 |
Sung J, Ghahramani Z, Bang SY. Latent-space variational bayes. Ieee Transactions On Pattern Analysis and Machine Intelligence. 30: 2236-42. PMID 18988955 DOI: 10.1109/TPAMI.2008.157 |
0.386 |
|
| 2008 |
Sung J, Ghahramani Z, Bang SY. Second-order latent-space variational bayes for approximate bayesian inference Ieee Signal Processing Letters. 15: 918-921. DOI: 10.1109/LSP.2008.2001557 |
0.354 |
|
| 2008 |
Zhang J, Ghahramani Z, Yang Y. Flexible latent variable models for multi-task learning Machine Learning. 73: 221-242. DOI: 10.1007/S10994-008-5050-1 |
0.385 |
|
| 2008 |
Van Gael J, Saatci Y, Teh YW, Ghahramani Z. Beam sampling for the infinite hidden Markov model Proceedings of the 25th International Conference On Machine Learning. 1088-1095. |
0.535 |
|
| 2007 |
Knowles D, Ghahramani Z. Infinite sparse factor analysis and infinite independent components analysis Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4666: 381-388. |
0.543 |
|
| 2007 |
Teh YW, Görür D, Ghahramani Z. Stick-breaking construction for the Indian buffet process Journal of Machine Learning Research. 2: 556-563. |
0.503 |
|
| 2007 |
Meeds E, Ghahramani Z, Neal R, Roweis S. Modeling dyadic data with binary latent factors Advances in Neural Information Processing Systems. 977-984. |
0.752 |
|
| 2006 |
Kim HC, Ghahramani Z. Bayesian Gaussian process classification with the EM-EP algorithm. Ieee Transactions On Pattern Analysis and Machine Intelligence. 28: 1948-59. PMID 17108369 DOI: 10.1109/TPAMI.2006.238 |
0.346 |
|
| 2006 |
Chu W, Ghahramani Z, Podtelezhnikov A, Wild DL. Bayesian segmental models with multiple sequence alignment profiles for protein secondary structure and contact map prediction. Ieee/Acm Transactions On Computational Biology and Bioinformatics / Ieee, Acm. 3: 98-113. PMID 17048397 DOI: 10.1109/TCBB.2006.17 |
0.349 |
|
| 2006 |
Beal MJ, Ghahramani Z. Variational Bayesian learning of directed graphical models with hidden variables Bayesian Analysis. 1: 793-832. DOI: 10.1214/06-BA126 |
0.441 |
|
| 2006 |
Wood F, Griffiths TL, Ghahramani Z. A non-parametric Bayesian method for inferring hidden causes Proceedings of the 22nd Conference On Uncertainty in Artificial Intelligence, Uai 2006. 536-543. |
0.425 |
|
| 2005 |
Penny W, Ghahramani Z, Friston K. Bilinear dynamical systems. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences. 360: 983-93. PMID 16087442 DOI: 10.1098/Rstb.2005.1642 |
0.345 |
|
| 2005 |
Beal MJ, Falciani F, Ghahramani Z, Rangel C, Wild DL. A Bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioinformatics (Oxford, England). 21: 349-56. PMID 15353451 DOI: 10.1093/bioinformatics/bti014 |
0.37 |
|
| 2005 |
Griffiths TL, Ghahramani Z. Infinite latent feature models and the Indian buffet process Advances in Neural Information Processing Systems. 475-482. |
0.47 |
|
| 2004 |
Todorov E, Ghahramani Z. Analysis of the synergies underlying complex hand manipulation. Conference Proceedings : ... Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual Conference. 6: 4637-40. PMID 17271341 DOI: 10.1109/IEMBS.2004.1404285 |
0.504 |
|
| 2004 |
Dubey A, Hwang S, Rangel C, Rasmussen CE, Ghahramani Z, Wild DL. Clustering protein sequence and structure space with infinite Gaussian mixture models. Pacific Symposium On Biocomputing. Pacific Symposium On Biocomputing. 399-410. PMID 14992520 DOI: 10.1142/9789812704856_0038 |
0.678 |
|
| 2004 |
Rangel C, Angus J, Ghahramani Z, Lioumi M, Sotheran E, Gaiba A, Wild DL, Falciani F. Modeling T-cell activation using gene expression profiling and state-space models. Bioinformatics (Oxford, England). 20: 1361-72. PMID 14962938 DOI: 10.1093/Bioinformatics/Bth093 |
0.319 |
|
| 2004 |
Thrun S, Liu Y, Koller D, Ng AY, Ghahramani Z, Durrant-Whyte H. Simultaneous localization and mapping with sparse extended information filters International Journal of Robotics Research. 23: 693-716. DOI: 10.1177/0278364904045479 |
0.523 |
|
| 2004 |
Thrun S, Koller D, Ghahramani Z, Durrant-Whyte H, Ng AY. Simultaneous mapping and localization with sparse extended information filters: Theory and initial results Springer Tracts in Advanced Robotics. 7: 363-380. DOI: 10.1007/978-3-540-45058-0_22 |
0.441 |
|
| 2003 |
Todorov E, Ghahramani Z. Unsupervised Learning of Sensory-Motor Primitives Annual International Conference of the Ieee Engineering in Medicine and Biology - Proceedings. 2: 1750-1753. |
0.537 |
|
| 2003 |
Salakhutdinov R, Roweis S, Ghahramani Z. Optimization with EM and Expectation-Conjugate-Gradient Proceedings, Twentieth International Conference On Machine Learning. 2: 672-679. |
0.718 |
|
| 2002 |
Ueda N, Ghahramani Z. Bayesian model search for mixture models based on optimizing variational bounds. Neural Networks : the Official Journal of the International Neural Network Society. 15: 1223-41. PMID 12425440 DOI: 10.1016/S0893-6080(02)00040-0 |
0.409 |
|
| 2002 |
Raval A, Ghahramani Z, Wild DL. A Bayesian network model for protein fold and remote homologue recognition. Bioinformatics (Oxford, England). 18: 788-801. PMID 12075014 DOI: 10.1093/Bioinformatics/18.6.788 |
0.337 |
|
| 2002 |
Beal MJ, Ghahramani Z, Rasmussen CE. The infinite hidden markov model Advances in Neural Information Processing Systems. |
0.709 |
|
| 2001 |
Wolpert DM, Ghahramani Z, Flanagan JR. Perspectives and problems in motor learning. Trends in Cognitive Sciences. 5: 487-494. PMID 11684481 DOI: 10.1016/S1364-6613(00)01773-3 |
0.553 |
|
| 2001 |
Ghahramani Z. An introduction to hidden Markov models and Bayesian networks International Journal of Pattern Recognition and Artificial Intelligence. 15: 9-42. DOI: 10.1142/S0218001401000836 |
0.424 |
|
| 2000 |
Wolpert DM, Ghahramani Z. Computational principles of movement neuroscience. Nature Neuroscience. 3: 1212-7. PMID 11127840 DOI: 10.1038/81497 |
0.54 |
|
| 2000 |
Ueda N, Nakano R, Ghahramani Z, Hinton GE. SMEM algorithm for mixture models. Neural Computation. 12: 2109-28. PMID 10976141 |
0.665 |
|
| 2000 |
Ghahramani Z, Hinton GE. Variational learning for switching state-space models. Neural Computation. 12: 831-64. PMID 10770834 DOI: 10.1162/089976600300015619 |
0.722 |
|
| 2000 |
Ueda N, Nakano R, Ghahramani Z, Hinton GE. Split and merge EM algorithm for improving Gaussian mixture density estimates Journal of Vlsi Signal Processing Systems For Signal, Image, and Video Technology. 26: 133-140. |
0.621 |
|
| 2000 |
Hinton GE, Ghahramani Z, Teh YW. Learning to parse images Advances in Neural Information Processing Systems. 463-469. |
0.719 |
|
| 1999 |
Roweis S, Ghahramani Z. A unifying review of linear gaussian models. Neural Computation. 11: 305-45. PMID 9950734 DOI: 10.1162/089976699300016674 |
0.791 |
|
| 1999 |
Jordan MI, Ghahramani Z, Jaakkola TS, Saul LK. Introduction to variational methods for graphical models Machine Learning. 37: 183-233. DOI: 10.1023/A:1007665907178 |
0.781 |
|
| 1999 |
Ghahramani Z, Roweis ST. Learning nonlinear dynamical systems using an EM algorithm Advances in Neural Information Processing Systems. 431-437. |
0.741 |
|
| 1998 |
Ghahramani Z, Hinton GE. Hierarchical non-linear factor analysis and topographic maps Advances in Neural Information Processing Systems. 486-492. |
0.614 |
|
| 1997 |
Hinton GE, Ghahramani Z. Generative models for discovering sparse distributed representations. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences. 352: 1177-90. PMID 9304685 DOI: 10.1098/rstb.1997.0101 |
0.715 |
|
| 1997 |
Ghahramani Z, Wolpert DM. Modular decomposition in visuomotor learning. Nature. 386: 392-5. PMID 9121554 DOI: 10.1038/386392a0 |
0.574 |
|
| 1997 |
Ghahramani Z, Wolpert DM, Michale I. J. Computational models of sensorimotor integration Advances in Psychology. 119: 117-147. DOI: 10.1016/S0166-4115(97)80006-4 |
0.559 |
|
| 1997 |
Ghahramani Z, Jordan MI. Factorial Hidden Markov Models Machine Learning. 29: 245-273. |
0.5 |
|
| 1996 |
Ghahramani Z, Wolpert DM, Jordan MI. Generalization to local remappings of the visuomotor coordinate transformation. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 16: 7085-96. PMID 8824344 DOI: 10.1523/Jneurosci.16-21-07085.1996 |
0.61 |
|
| 1996 |
Cohn DA, Ghahramani Z, Jordan MI. Active learning with statistical models Journal of Artificial Intelligence Research. 4: 129-145. DOI: 10.1613/Jair.295 |
0.55 |
|
| 1995 |
Wolpert DM, Ghahramani Z, Jordan MI. Are arm trajectories planned in kinematic or dynamic coordinates? An adaptation study. Experimental Brain Research. 103: 460-70. PMID 7789452 DOI: 10.1007/BF00241505 |
0.621 |
|
| 1995 |
Wolpert DM, Ghahramani Z, Jordan MI. An internal model for sensorimotor integration. Science (New York, N.Y.). 269: 1880-2. PMID 7569931 DOI: 10.1126/Science.7569931 |
0.654 |
|
| 1994 |
Wolpert DM, Ghahramani Z, Jordan MI. Perceptual distortion contributes to the curvature of human reaching movements. Experimental Brain Research. 98: 153-6. PMID 8013583 DOI: 10.1007/BF00229120 |
0.599 |
|
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