A sparse covariance function for exact Gaussian process inference in large datasets A Melkumyan, FT Ramos Twenty-first international joint conference on artificial intelligence, 2009 | 152 | 2009 |
Multi-kernel Gaussian processes A Melkumyan, F Ramos Twenty-second international joint conference on artificial intelligence, 2011 | 110 | 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 | 77 | 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 | 69 | 2018 |
Influence of imperfect bonding on interface waves guided by piezoelectric/piezomagnetic composites A Melkumyan, YW Mai Philosophical Magazine 88 (23), 2965-2977, 2008 | 64 | 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 | 49 | 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 | 48 | 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 | 47 | 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 | 43 | 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 | 38 | 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 | 35 | 2015 |
Method and system of data modelling A Melkumyan, FT Ramos US Patent 8,849,622, 2014 | 30 | 2014 |
t-SNE based visualisation and clustering of geological domain M Balamurali, A Melkumyan Neural Information Processing: 23rd International Conference, ICONIP 2016 …, 2016 | 29 | 2016 |
Hyperspectral CNN classification with limited training samples L Windrim, R Ramakrishnan, A Melkumyan, R Murphy arXiv preprint arXiv:1611.09007, 2016 | 24 | 2016 |
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 | 24 | 2016 |
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 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 | 17 | 2019 |
Unsupervised feature learning for illumination robustness L Windrim, A Melkumyan, R Murphy, A Chlingaryan, J Nieto 2016 IEEE International Conference on Image Processing (ICIP), 4453-4457, 2016 | 17 | 2016 |
Sample truncation strategies for outlier removal in geochemical data: the MCD robust distance approach versus t-SNE ensemble clustering R Leung, M Balamurali, A Melkumyan Mathematical Geosciences 53, 105-130, 2021 | 14 | 2021 |