| Deep Gaussian processes A C. Damianou, N D. Lawrence Proceedings of the Sixteenth International Workshop on Artificial …, 2013 | 1584 | 2013 |
| Variational information distillation for knowledge transfer S Ahn, SX Hu, A Damianou, ND Lawrence, Z Dai Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 836 | 2019 |
| Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling P Perdikaris, M Raissi, A Damianou, ND Lawrence, GE Karniadakis Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2017 | 475 | 2017 |
| Variational inference for latent variables and uncertain inputs in Gaussian processes AC Damianou, MK Titsias, N Lawrence Journal of Machine Learning Research (JMLR), 2016 | 222* | 2016 |
| Variational auto-encoded deep Gaussian processes Z Dai, A Damianou, J González, N Lawrence International Conference on Learning Representations (ICLR), 2015 | 185 | 2015 |
| Empirical bayes transductive meta-learning with synthetic gradients SX Hu, PG Moreno, Y Xiao, X Shen, G Obozinski, ND Lawrence, ... International Conference on Learning Representation (ICLR), 2020 | 181 | 2020 |
| Deep Gaussian processes for multi-fidelity modeling K Cutajar, M Pullin, A Damianou, N Lawrence, J González arXiv preprint arXiv:1903.07320, 2019 | 158 | 2019 |
| Manifold relevance determination A Damianou, CH Ek, M Titsias, N Lawrence Proceedings of the 29th International Conference on Machine Learning, 145-152, 2012 | 155 | 2012 |
| Variational gaussian process dynamical systems A Damianou, MK Titsias, ND Lawrence Advances in Neural Information Processing Systems, 2510-2518, 2011 | 147 | 2011 |
| Deep Gaussian processes and variational propagation of uncertainty A Damianou University of Sheffield, 2015 | 143 | 2015 |
| Preferential bayesian optimization J González, Z Dai, A Damianou, ND Lawrence International Conference on Machine Learning, 1282-1291, 2017 | 136 | 2017 |
| DAC-h3: a proactive robot cognitive architecture to acquire and express knowledge about the world and the self C Moulin-Frier, T Fischer, M Petit, G Pointeau, JY Puigbo, U Pattacini, ... IEEE Transactions on Cognitive and Developmental Systems 10 (4), 1005-1022, 2017 | 94 | 2017 |
| Active learning for sparse bayesian multilabel classification D Vasisht, A Damianou, M Varma, A Kapoor Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014 | 88 | 2014 |
| Leveraging crowdsourcing data for deep active learning an application: Learning intents in alexa J Yang, T Drake, A Damianou, Y Maarek Proceedings of the 2018 World Wide Web Conference, 23-32, 2018 | 79 | 2018 |
| Transferring knowledge across learning processes S Flennerhag, PG Moreno, ND Lawrence, A Damianou International Conference on Learning Representations (ICLR), 2018 | 77 | 2018 |
| Comprehensive landscape of active deubiquitinating enzymes profiled by advanced chemoproteomics A Pinto-Fernández, S Davis, AB Schofield, HC Scott, P Zhang, E Salah, ... Frontiers in chemistry 7, 592, 2019 | 60 | 2019 |
| Orl: Reinforcement learning benchmarks for online stochastic optimization problems B Balaji, J Bell-Masterson, E Bilgin, A Damianou, PM Garcia, A Jain, ... arXiv preprint arXiv:1911.10641, 2019 | 55 | 2019 |
| Fast adaptation with linearized neural networks W Maddox, S Tang, P Moreno, AG Wilson, A Damianou International Conference on Artificial Intelligence and Statistics, 2737-2745, 2021 | 47 | 2021 |
| Memory and mental time travel in humans and social robots TJ Prescott, D Camilleri, U Martinez-Hernandez, A Damianou, ... Philosophical Transactions of the Royal Society B 374 (1771), 20180025, 2019 | 46 | 2019 |
| Semi-described and semi-supervised learning with Gaussian processes A Damianou, ND Lawrence 31st Conference on Uncertainty in Artificial Intelligence (UAI), 2015 | 38 | 2015 |
Neil D. LawrenceUniversity of Cambridge確認したメール アドレス: cl.cam.ac.uk