Andreas Damianou
Andreas Damianou
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Deep Gaussian processes
A C. Damianou, N D. Lawrence
Proceedings of the Sixteenth International Workshop on Artificial†…, 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
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
Variational inference for latent variables and uncertain inputs in Gaussian processes
AC Damianou, MK Titsias, N Lawrence
Manifold relevance determination
A Damianou, CH Ek, M Titsias, N Lawrence
Proceedings of the 29th International Conference on Machine Learning, 145-152, 2012
Variational auto-encoded deep Gaussian processes
Z Dai, A Damianou, J GonzŠlez, N Lawrence
arXiv preprint arXiv:1511.06455, 2015
Variational gaussian process dynamical systems
A Damianou, MK Titsias, ND Lawrence
Advances in Neural Information Processing Systems, 2510-2518, 2011
Deep Gaussian processes and variational propagation of uncertainty
A Damianou
University of Sheffield, 2015
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
Empirical bayes transductive meta-learning with synthetic gradients
SX Hu, PG Moreno, Y Xiao, X Shen, G Obozinski, ND Lawrence, ...
arXiv preprint arXiv:2004.12696, 2020
Deep gaussian processes for multi-fidelity modeling
K Cutajar, M Pullin, A Damianou, N Lawrence, J GonzŠlez
arXiv preprint arXiv:1903.07320, 2019
Preferential bayesian optimization
J GonzŠlez, Z Dai, A Damianou, ND Lawrence
International Conference on Machine Learning, 1282-1291, 2017
Transferring knowledge across learning processes
S Flennerhag, PG Moreno, ND Lawrence, A Damianou
arXiv preprint arXiv:1812.01054, 2018
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
Semi-described and semi-supervised learning with Gaussian processes
A Damianou, ND Lawrence
31st Conference on Uncertainty in Artificial Intelligence (UAI), 2015
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
An integrated probabilistic framework for robot perception, learning and memory
U Martinez-Hernandez, A Damianou, D Camilleri, LW Boorman, ...
2016 IEEE International Conference on Robotics and Biomimetics (ROBIO), 1796†…, 2016
Gaussian process models with parallelization and GPU acceleration
Z Dai, A Damianou, J Hensman, N Lawrence
arXiv preprint arXiv:1410.4984, 2014
Comprehensive landscape of active deubiquitinating enzymes profiled by advanced chemoproteomics
A Pinto-FernŠndez, S Davis, AB Schofield, HC Scott, P Zhang, E Salah, ...
Frontiers in chemistry 7, 592, 2019
Deep recurrent Gaussian processes for outlier-robust system identification
CLC Mattos, Z Dai, A Damianou, GA Barreto, ND Lawrence
Journal of Process Control 60, 82-94, 2017
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