| A sparse covariance function for exact Gaussian process inference in large datasets. A Melkumyan, F Ramos IJCAI 9, 1936-1942, 2009 | 164 | 2009 |
| Multi-kernel Gaussian processes A Melkumyan, F Ramos IJCAI Proceedings-International Joint Conference on Artificial Intelligence …, 2011 | 117 | 2011 |
| Twelve shear surface waves guided by clamped/free boundaries in magneto-electro-elastic materials A Melkumyan International Journal of Solids and Structures 44 (10), 3594-3599, 2007 | 81 | 2007 |
| Pretraining for hyperspectral convolutional neural network classification L Windrim, A Melkumyan, RJ Murphy, A Chlingaryan, R Ramakrishnan IEEE Transactions on Geoscience and Remote Sensing 56 (5), 2798-2810, 2018 | 73 | 2018 |
| Influence of imperfect bonding on interface waves guided by piezoelectric/piezomagnetic composites A Melkumyan, YW Mai Philosophical Magazine 88 (23), 2965-2977, 2008 | 69 | 2008 |
| Unsupervised feature-learning for hyperspectral data with autoencoders L Windrim, R Ramakrishnan, A Melkumyan, RJ Murphy, A Chlingaryan Remote Sensing 11 (7), 864, 2019 | 54 | 2019 |
| A physics-based deep learning approach to shadow invariant representations of hyperspectral images L Windrim, R Ramakrishnan, A Melkumyan, RJ Murphy IEEE Transactions on Image Processing 27 (2), 665-677, 2017 | 50 | 2017 |
| Evaluating the performance of a new classifier–the GP-OAD: A comparison with existing methods for classifying rock type and mineralogy from hyperspectral imagery S Schneider, RJ Murphy, A Melkumyan ISPRS journal of photogrammetry and remote sensing 98, 145-156, 2014 | 49 | 2014 |
| A novel endmember bundle extraction and clustering approach for capturing spectral variability within endmember classes T Uezato, RJ Murphy, A Melkumyan, A Chlingaryan IEEE Transactions on Geoscience and Remote Sensing 54 (11), 6712-6731, 2016 | 46 | 2016 |
| On the linear and nonlinear observability analysis of the SLAM problem LDL Perera, A Melkumyan, E Nettleton 2009 IEEE International Conference on Mechatronics, 1-6, 2009 | 41 | 2009 |
| A novel spectral unmixing method incorporating spectral variability within endmember classes T Uezato, RJ Murphy, A Melkumyan, A Chlingaryan IEEE Transactions on Geoscience and Remote Sensing 54 (5), 2812-2831, 2015 | 39 | 2015 |
| Automated recognition of stratigraphic marker shales from geophysical logs in iron ore deposits K Silversides, A Melkumyan, D Wyman, P Hatherly Computers & Geosciences 77, 118-125, 2015 | 39 | 2015 |
| t-SNE based visualisation and clustering of geological domain M Balamurali, A Melkumyan Neural Information Processing: 23rd International Conference, ICONIP 2016 …, 2016 | 32 | 2016 |
| Method and system of data modelling A Melkumyan, FT Ramos US Patent 8,849,622, 2014 | 31 | 2014 |
| Incorporating spatial information and endmember variability into unmixing analyses to improve abundance estimates T Uezato, RJ Murphy, A Melkumyan, A Chlingaryan IEEE Transactions on Image Processing 25 (12), 5563-5575, 2016 | 25 | 2016 |
| Hyperspectral CNN classification with limited training samples L Windrim, R Ramakrishnan, A Melkumyan, R Murphy arXiv preprint arXiv:1611.09007, 2016 | 24 | 2016 |
| A comparison of t-SNE, SOM and SPADE for identifying material type domains in geological data M Balamurali, KL Silversides, A Melkumyan Computers & Geosciences 125, 78-89, 2019 | 21 | 2019 |
| Gaussian processes with OAD covariance function for hyperspectral data classification S Schneider, A Melkumyan, RJ Murphy, E Nettleton 2010 22nd IEEE International Conference on Tools with Artificial …, 2010 | 18 | 2010 |
| A machine learning approach for material type logging and chemical assaying from autonomous measure-while-drilling (MWD) data RN Khushaba, A Melkumyan, AJ Hill Mathematical Geosciences 54 (2), 285-315, 2022 | 16 | 2022 |
| Boundary identification and surface updates using mwd KL Silversides, A Melkumyan Mathematical Geosciences 53 (5), 1047-1071, 2021 | 16 | 2021 |
Anna ChlingaryanResearch Fellow, the University of SydneyVerified email at acfr.usyd.edu.au
