| On social networks and collaborative recommendation I Konstas, V Stathopoulos, JM Jose Proceedings of the 32nd international ACM SIGIR conference on Research and …, 2009 | 737 | 2009 |
| Acoustic identification of Mexican bats based on taxonomic and ecological constraints on call design V Zamora‐Gutierrez, C Lopez‐Gonzalez, MC MacSwiney Gonzalez, ... Methods in Ecology and Evolution 7 (9), 1082-1091, 2016 | 51 | 2016 |
| Markov chain Monte Carlo inference for Markov jump processes via the linear noise approximation V Stathopoulos, MA Girolami Philosophical Transactions of the Royal Society A: Mathematical, Physical …, 2013 | 45 | 2013 |
| Markov chain Monte Carlo inference for Markov jump processes via the linear noise approximation V Stathopoulos, MA Girolami Philosophical Transactions of the Royal Society A: Mathematical, Physical …, 2013 | 45 | 2013 |
| EWS-FLI1 employs an E2F switch to drive target gene expression R Schwentner, T Papamarkou, MO Kauer, V Stathopoulos, F Yang, ... Nucleic acids research 43 (5), 2780-2789, 2015 | 31 | 2015 |
| Putting the scientist in the loop--Accelerating scientific progress with interactive machine learning O Mac Aodha, V Stathopoulos, GJ Brostow, M Terry, M Girolami, ... 2014 22nd International Conference on Pattern Recognition, 9-17, 2014 | 21 | 2014 |
| Bat echolocation call identification for biodiversity monitoring: a probabilistic approach V Stathopoulos, V Zamora‐Gutierrez, KE Jones, M Girolami Journal of the Royal Statistical Society: Series C (Applied Statistics) 67 …, 2018 | 17 | 2018 |
| Bat call identification with gaussian process multinomial probit regression and a dynamic time warping kernel V Stathopoulos, V Zamora-Gutierrez, K Jones, M Girolami Artificial intelligence and statistics, 913-921, 2014 | 17 | 2014 |
| Lagrangian Dynamical Monte Carlo S Lan, V Stathopoulos, B Shahbaba, M Girolami arXiv preprint arXiv:1211.3759, 2012 | 16 | 2012 |
| A framework for evaluating automatic image annotation algorithms K Athanasakos, V Stathopoulos, JM Jose European Conference on Information Retrieval, 217-228, 2010 | 14 | 2010 |
| Bayesian mixture hierarchies for automatic image annotation V Stathopoulos, JM Jose European Conference on Information Retrieval, 138-149, 2009 | 14 | 2009 |
| Semantic relationships in multi-modal graphs for automatic image annotation V Stathopoulos, J Urban, J Jose European Conference on Information Retrieval, 490-497, 2008 | 5 | 2008 |
| mcmc_clib –an advanced MCMC sampling package for ode models A Kramer, V Stathopoulos, M Girolami, N Radde Bioinformatics 30 (20), 2991-2992, 2014 | 4 | 2014 |
| Manifold MCMC for mixtures V Stathopoulos, M Girolami Mixture Estimation and Applications, 255-276, 2011 | 4 | 2011 |
| A simulated user study of image browsing using high-level classification T Leelanupab, Y Feng, V Stathopoulos, JM Jose International Conference on Semantic and Digital Media Technologies, 3-15, 2009 | 4 | 2009 |
| Glasgow University at TRECVID 2008. P Punitha, T Urruty, Y Feng, M Halvey, A Goyal, D Hannah, IA Klampanos, ... TRECVID, 2008 | 4 | 2008 |
| Bayesian probabilistic models for image retrieval V Stathopoulos, JM Jose Proceedings of the Second Workshop on Applications of Pattern Analysis, 41-47, 2011 | 3 | 2011 |
| A Knowledge-Based Reverse Design System for Declarative Scene Modeling. V Golfinopoulos, VM Stathopoulos, G Miaoulis, D Plemenos ICEIS (2), 82-90, 2006 | 2 | 2006 |
| Generative probabilistic models for image retrieval V Stathopoulos University of Glasgow, 2012 | 1 | 2012 |
| Pattern Classification with Gaussian Processes V Stathopoulos, M Girolami Pattern Recognition And Big Data, 37-73, 2017 | | 2017 |
