| Venture: a higher-order probabilistic programming platform with programmable inference V Mansinghka, D Selsam, Y Perov arXiv preprint arXiv:1404.0099, 2014 | 168 | 2014 |
| Approximate bayesian image interpretation using generative probabilistic graphics programs VK Mansinghka, TD Kulkarni, YN Perov, J Tenenbaum Advances in Neural Information Processing Systems, 1520-1528, 2013 | 87 | 2013 |
| A comparative study of artificial intelligence and human doctors for the purpose of triage and diagnosis S Razzaki, A Baker, Y Perov, K Middleton, J Baxter, D Mullarkey, ... arXiv preprint arXiv:1806.10698, 2018 | 34 | 2018 |
| Gait optimization for roombots modular robots—Matching simulation and reality R Moeckel, YN Perov, AT Nguyen, M Vespignani, S Bonardi, S Pouya, ... 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2013 | 19 | 2013 |
| A universal marginalizer for amortized inference in generative models L Douglas, I Zarov, K Gourgoulias, C Lucas, C Hart, A Baker, M Sahani, ... arXiv preprint arXiv:1711.00695, 2017 | 10 | 2017 |
| Venture: a higher-order probabilistic programming platform with programmable inference. arXiv V Mansinghka, D Selsam, Y Perov March, 2014 | 10 | 2014 |
| Venture: a higher-order probabilistic programming platform with programmable inference. 2014 VK Mansinghka, D Selsam, YN Perov arXiv preprint arXiv:1404.0099, 2014 | 8 | 2014 |
| Automatic sampler discovery via probabilistic programming and approximate bayesian computation Y Perov, F Wood International Conference on Artificial General Intelligence, 262-273, 2016 | 7 | 2016 |
| Multiverse: causal reasoning using importance sampling in probabilistic programming Y Perov, L Graham, K Gourgoulias, J Richens, C Lee, A Baker, S Johri Symposium on Advances in Approximate Bayesian Inference, 1-36, 2020 | 5 | 2020 |
| Data-driven sequential Monte Carlo in probabilistic programming YN Perov, TA Le, F Wood arXiv preprint arXiv:1512.04387, 2015 | 5 | 2015 |
| Learning probabilistic programs YN Perov, FD Wood arXiv preprint arXiv:1407.2646, 2014 | 5 | 2014 |
| Anh The Nguyen, Massimo Vespignani, Stephane Bonardi, Soha Pouya, Alexander Spröwitz, Jesse van den Kieboom, Frederic Wilhelm, and Auke Jan Ijspeert. Gait optimization for … R Moeckel, NP Yura Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent …, 2013 | 5 | 2013 |
| Applications of Probabilistic Programming (Master's thesis, 2015) YN Perov arXiv preprint arXiv:1606.00075, 2016 | 4 | 2016 |
| Computing engine, software, system and method F Wood, M Wu, Y Perov, H Yang US Patent App. 15/465,131, 2017 | 3 | 2017 |
| Universal marginalizer for amortised inference and embedding of generative models R Walecki, A Buchard, K Gourgoulias, C Hart, M Lomeli, AKW Navarro, ... arXiv preprint arXiv:1811.04727, 2018 | 2 | 2018 |
| Nonparametric Bayesian models for unsupervised activity recognition and tracking N Dhir, Y Perov, F Wood 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2016 | 2 | 2016 |
| Spreadsheet probabilistic programming M Wu, Y Perov, F Wood, H Yang arXiv preprint arXiv:1606.04216, 2016 | 2 | 2016 |
| Learning medical triage from clinicians using Deep Q-Learning A Buchard, B Bouvier, G Prando, R Beard, M Livieratos, D Busbridge, ... arXiv preprint arXiv:2003.12828, 2020 | 1 | 2020 |
| Applications of probabilistic programming Y Perov University of Oxford, 2015 | 1 | 2015 |
| Exploiting Conditional Independence for Efficient, Automatic Multicore Inference for Church Y Perov, V Mansinghka NIPS 2012 Workshop on Probabilistic Programming, 2012 | 1 | 2012 |
Vikash K. MansinghkaMIT, Probabilistic Computing ProjectVerified email at mit.edu
