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Hiranmayi Ranganathan
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Multimodal emotion recognition using deep learning architectures
H Ranganathan, S Chakraborty, S Panchanathan
2016 IEEE winter conference on applications of computer vision (WACV), 1-9, 2016
2342016
Deep active learning for image classification
H Ranganathan, H Venkateswara, S Chakraborty, S Panchanathan
2017 IEEE International Conference on Image Processing (ICIP), 3934-3938, 2017
942017
Deep Active Learning for Image Regression
H Ranganathan, H Venkateswara, S Chakraborty, S Panchanathan
Deep Learning Applications. Advances in Intelligent Systems and Computing …, 2020
152020
Transfer of multimodal emotion features in deep belief networks
H Ranganathan, S Chakraborty, S Panchanathan
2016 50th Asilomar Conference on Signals, Systems and Computers, 449-453, 2016
92016
Multi-label deep active learning with label correlation
H Ranganathan, H Venkateswara, S Chakraborty, S Panchanathan
2018 25th IEEE International Conference on Image Processing (ICIP), 3418-3422, 2018
62018
Wavelet domain mutual information synchronization of multimodal cardiac microscopy image sequences
M Liebling, H Ranganathan
Wavelets XIII 7446, 11-15, 2009
62009
Deep Active Learning Explored Across Diverse Label Spaces
H Ranganathan
Arizona State University, 2018
12018
ndicators of Profile-QSAR success.
S He, KS Mcloughlin, H Ranganathan, JE Allen, D Shi, S Kim
bioRxiv, 2022
2022
Model Choice Metrics to Optimize Profile-QSAR Performance
S He, S Kim, KS McLoughlin, H Ranganathan, D Shi, JE Allen
bioRxiv, 2022.08. 22.504151, 2022
2022
Active Learning for NLP Systems
AR Goncalves, A Sales, H Ranganathan, BC Soper, P Ray
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States), 2019
2019
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Articles 1–10