Kin Gwn Lore
Kin Gwn Lore
Senior Research Engineer, Raytheon Technologies Research Center
Verified email at rtx.com
Title
Cited by
Cited by
Year
LLNet: A deep autoencoder approach to natural low-light image enhancement
KG Lore, A Akintayo, S Sarkar
Pattern Recognition 61, 650-662, 2017
2642017
Deep learning for flow sculpting: Insights into efficient learning using scientific simulation data
D Stoecklein, KG Lore, M Davies, S Sarkar, B Ganapathysubramanian
Scientific reports 7, 46368, 2017
322017
Early detection of combustion instability from hi-speed flame images via deep learning and symbolic time series analysis
S Sarkar, KG Lore, S Sarkar, V Ramanan, SR Chakravarthy, S Phoha, ...
Annual Conf. of the Prognostics and Health Management, 2015
312015
Prognostics of combustion instabilities from hi-speed flame video using a deep convolutional selective autoencoder
A Akintayo, KG Lore, S Sarkar, S Sarkar
International Journal of Prognostics and Health Management 7 (023), 1-14, 2016
242016
Early Detection of Combustion Instability by Neural-Symbolic Analysis on Hi-Speed Video.
S Sarkar, KG Lore, S Sarkar
CoCo@ NIPS, 2015
232015
Hierarchical feature extraction for efficient design of microfluidic flow patterns
KG Lore, D Stoecklein, M Davies, B Ganapathysubramanian, S Sarkar
Feature Extraction: Modern Questions and Challenges, 213-225, 2015
232015
A deep learning framework for causal shape transformation
KG Lore, D Stoecklein, M Davies, B Ganapathysubramanian, S Sarkar
Neural Networks 98, 305-317, 2018
182018
Deep value of information estimators for collaborative human-machine information gathering
KG Lore, N Sweet, K Kumar, N Ahmed, S Sarkar
2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS …, 2016
142016
Generative adversarial networks for depth map estimation from RGB video
KG Lore, K Reddy, M Giering, EA Bernal
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018
122018
Early detection of combustion instabilities using deep convolutional selective autoencoders on hi-speed flame video
A Akintayo, KG Lore, S Sarkar, S Sarkar
arXiv preprint arXiv:1603.07839, 2016
122016
A deep 3d convolutional neural network based design for manufacturability framework
A Balu, KG Lore, G Young, A Krishnamurthy, S Sarkar
arXiv preprint arXiv:1612.02141, 2016
10*2016
Multimodal spatiotemporal information fusion using neural-symbolic modeling for early detection of combustion instabilities
S Sarkar, DK Jha, KG Lore, S Sarkar, A Ray
2016 American Control Conference (ACC), 4918-4923, 2016
62016
Data-driven root-cause analysis for distributed system anomalies
C Liu, KG Lore, S Sarkar
2017 IEEE 56th Annual Conference on Decision and Control (CDC), 5745-5750, 2017
52017
Generative Adversarial Networks for Spectral Super-Resolution and Bidirectional RGB-To-Multispectral Mapping
KG Lore, KK Reddy, M Giering, EA Bernal
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
32019
Deep action sequence learning for causal shape transformation
KG Lore, D Stoecklein, M Davies, B Ganapathysubramanian, S Sarkar
arXiv preprint arXiv:1605.05368, 2016
32016
Physics-based features for anomaly detection in power grids with micro-pmus
M El Chamie, KG Lore, DM Shila, A Surana
2018 IEEE International Conference on Communications (ICC), 1-7, 2018
22018
Root-cause analysis for time-series anomalies via spatiotemporal causal graphical modeling
C Liu, KG Lore, S Sarkar
arXiv preprint arXiv:1605.06421, 2016
22016
Detecting Data Integrity Attacks on Correlated Solar Farms Using Multi-layer Data Driven Algorithm
KG Lore, DM Shila, L Ren
2018 IEEE Conference on Communications and Network Security (CNS), 1-9, 2018
12018
SYSTEM AND METHOD FOR CONTEXT-BASED TRAINING OF A MACHINE LEARNING MODEL
KG Lore, KK Reddy
US Patent App. 16/253,366, 2020
2020
Virtual sensor for elevator monitoring
S Sarkar, KK Reddy, KG Lore, GS Ekladious
US Patent App. 16/720,062, 2020
2020
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