chetak kandaswamy
chetak kandaswamy
VITO nv
Verified email at vito.be
Title
Cited by
Cited by
Year
High-content analysis of breast cancer using single-cell deep transfer learning
C Kandaswamy, LM Silva, LA Alexandre, JM Santos
Journal of biomolecular screening 21 (3), 252-259, 2016
482016
Using different cost functions to train stacked auto-encoders
T Amaral, LM Silva, LA Alexandre, C Kandaswamy, JM Santos, JM de Sá
Mexican International Conference on Artificial Intelligence (MICAI), 2013 …, 2013
472013
Improving deep neural network performance by reusing features trained with transductive transference
C Kandaswamy, LM Silva, LA Alexandre, JM Santos, JM de Sá
International Conference on Artificial Neural Networks, 265-272, 2014
372014
Improving transfer learning accuracy by reusing stacked denoising autoencoders
C Kandaswamy, LM Silva, LA Alexandre, R Sousa, JM Santos, JM de Sá
International Conference on Systems, Man, and Cybernetics (SMC), 1380-1387, 2014
312014
Multi-source deep transfer learning for cross-sensor biometrics
C Kandaswamy, JC Monteiro, LM Silva, JS Cardoso
Neural Computing and Applications 28 (9), 2461-2475, 2017
202017
Transfer learning using rotated image data to improve deep neural network performance
T Amaral, LM Silva, LA Alexandre, C Kandaswamy, JM de Sá, JM Santos
International Conference Image Analysis and Recognition, 290-300, 2014
202014
Improving performance on problems with few labelled data by reusing stacked auto-encoders
T Amaral, C Kandaswamy, LM Silva, LA Alexandre, J Marques De Sa, ...
Machine Learning and Applications (ICMLA), 2014 13th International …, 2014
142014
Source-target-source classification using stacked denoising autoencoders
C Kandaswamy, LM Silva, JS Cardoso
Iberian Conference on Pattern Recognition and Image Analysis, 39-47, 2015
82015
Deep Transfer Learning Ensemble for Classification
C Kandaswamy, LM Silva, LA Alexandre, JM Santos
International Work-Conference on Artificial Neural Networks, 335-348, 2015
82015
Speedup of deep learning ensembles for semantic segmentation using a model compression technique
A Holliday, M Barekatain, J Laurmaa, C Kandaswamy, H Prendinger
Computer Vision and Image Understanding 164, 16-26, 2017
72017
Speedup of deep learning ensembles for semantic segmentation using a model compression technique
A Holliday, M Barekatain, J Laurmaa, C Kandaswamy, H Prendinger
Computer Vision and Image Understanding 164, 16-26, 2017
72017
Transfer Learning: Current Status, Trends and Challenges
R Sousa, LM Silva, LA Alexandre, J Santos, JM De Sá
20th Portuguese Conference on Pattern Recognition, RecPad, 57-58, 2014
42014
Luıs. Alexandre, Ricardo Sousa, JM Santos, and J. Marques de Sá. Improving Transfer Learning Accuracy by Reusing Stacked Denoising Autoencoders. Systems Man and Cybernetics
C Kandaswamy, L Silva
IEEE Conference on. IEEE, 2014
22014
Tuning parameters of deep neural network algorithms for identifying best cost function
C Kandaswamy, T Amaral
Technical Report 2/2013, Instituto de Engenharia Biomédica/NNIG, 2013
22013
Report: Improving CNN by Reusing Features Trained with Transductive Transfer Setting
C Kandaswamy, L Silva, L Alexandre
Technical Report 2/2014, Instituto de Engenharia Biomedica/NNIG, Febyrary …, 0
1
Contributions on Deep Transfer Learning
C Kandaswamy
2016
Activities report from March 2014 to March 2015
C Kandaswamy
2015
Improve Performance in Deep Neural Networks:(1) Cost Functions, and (2) Reusable learning
C Kandaswamy
2014
Deep Transfer Learning Ensemble for Classification
C Kandaswamy123, LM Silva24, LA Alexandre, JM Santos26
Oral Sessions: Pattern Recognition and Machine Learning
A González, D Vázquez, S Ramos, AM López, J Amores, EM Pereira, ...
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