Kin Gwn Lore

According to our database, Kin Gwn Lore authored at least 19 papers between 2015 and 2021.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2021

Root-cause analysis for time-series anomalies via spatiotemporal graphical modeling in distributed complex systems.

Chao Liu, Kin Gwn Lore, Zhanhong Jiang, Soumik Sarkar

Knowl. Based Syst., 2021

2019

Implicit Context-aware Learning and Discovery for Streaming Data Analytics.

Kin Gwn Lore, Kishore K. Reddy

CoRR, 2019

Generative Adversarial Networks for Spectral Super-Resolution and Bidirectional RGB-To-Multispectral Mapping.

Kin Gwn Lore, Kishore K. Reddy, Michael Giering, Edgar A. Bernal

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019

2018

A deep learning framework for causal shape transformation.

Kin Gwn Lore, Daniel Stoecklein, Michael Davies, Baskar Ganapathysubramanian, Soumik Sarkar

Neural Networks, 2018

Physics-Based Features for Anomaly Detection in Power Grids with Micro-PMUs.

Mahmoud El Chamie, Kin Gwn Lore, Devu Manikantan Shila, Amit Surana

Proceedings of the 2018 IEEE International Conference on Communications, 2018

Generative Adversarial Networks for Depth Map Estimation From RGB Video.

Kin Gwn Lore, Kishore K. Reddy, Michael Giering, Edgar A. Bernal

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018

Detecting Data Integrity Attacks on Correlated Solar Farms Using Multi-layer Data Driven Algorithm.

Kin Gwn Lore, Devu Manikantan Shila, Lingyu Ren

Proceedings of the 2018 IEEE Conference on Communications and Network Security, 2018

2017

LLNet: A deep autoencoder approach to natural low-light image enhancement.

Kin Gwn Lore, Adedotun Akintayo, Soumik Sarkar

Pattern Recognit., 2017

Catching Anomalous Distributed Photovoltaics: An Edge-based Multi-modal Anomaly Detection.

Devu Manikantan Shila, Kin Gwn Lore, Tianshu Wei, Teems Lovetty, Yu Cheng

CoRR, 2017

Data-driven root-cause analysis for distributed system anomalies.

Chao Liu, Kin Gwn Lore, Soumik Sarkar

Proceedings of the 56th IEEE Annual Conference on Decision and Control, 2017

2016

Deep Action Sequence Learning for Causal Shape Transformation.

Kin Gwn Lore, Daniel Stoecklein, Michael Davies, Baskar Ganapathysubramanian, Soumik Sarkar

CoRR, 2016

Root-cause analysis for time-series anomalies via spatiotemporal causal graphical modeling.

Chao Liu, Kin Gwn Lore, Soumik Sarkar

CoRR, 2016

A Deep 3D Convolutional Neural Network Based Design for Manufacturability Framework.

Aditya Balu, Kin Gwn Lore, Gavin Young, Adarsh Krishnamurthy, Soumik Sarkar

CoRR, 2016

Early Detection of Combustion Instabilities using Deep Convolutional Selective Autoencoders on Hi-speed Flame Video.

Adedotun Akintayo, Kin Gwn Lore, Soumalya Sarkar, Soumik Sarkar

CoRR, 2016

Deep Value of Information Estimators for Collaborative Human-Machine Information Gathering.

Kin Gwn Lore, Nicholas Sweet, Kundan Kumar, Nisar R. Ahmed, Soumik Sarkar

Proceedings of the 7th ACM/IEEE International Conference on Cyber-Physical Systems, 2016

Multimodal spatiotemporal information fusion using neural-symbolic modeling for early detection of combustion instabilities.

Soumalya Sarkar, Devesh K. Jha, Kin Gwn Lore, Soumik Sarkar, Asok Ray

Proceedings of the 2016 American Control Conference, 2016

Topology control in mobile sensor networks using information space feedback.

Kin Gwn Lore, Soumik Sarkar, Devesh K. Jha

Proceedings of the 2016 American Control Conference, 2016

2015

Early Detection of Combustion Instability by Neural-Symbolic Analysis on Hi-Speed Video.

Soumalya Sarkar, Kin Gwn Lore, Soumik Sarkar

Proceedings of the NIPS Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches co-located with the 29th Annual Conference on Neural Information Processing Systems (NIPS 2015), 2015

Hierarchical Feature Extraction for Efficient Design of Microfluidic Flow Patterns.

Kin Gwn Lore, Daniel Stoecklein, Michael Davies, Baskar Ganapathysubramanian, Soumik Sarkar

Proceedings of the 1st Workshop on Feature Extraction: Modern Questions and Challenges, 2015