Lasitha Vidyaratne
Orcid: 0000-0003-4053-7948Affiliations:
- Hitachi America Ltd., Santa Clara, CA, USA
- Thomas Jefferson National Accelerator Facility, Newport News, VA, USA
- Old Dominion University, Norfolk, VA, USA (PhD 2020)
According to our database1,
Lasitha Vidyaratne
authored at least 31 papers
between 2014 and 2023.
Collaborative distances:
Collaborative distances:
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Bibliography
2023
Uncertainty Aware Deep Learning for Fault Prediction Using Multivariate Time Series Signals.
Proceedings of the International Joint Conference on Neural Networks, 2023
An ensemble of convolution-based methods for fault detection using vibration signals.
Proceedings of the IEEE International Conference on Prognostics and Health Management, 2023
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
2022
Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data With Spatial Information.
IEEE Trans. Neural Networks Learn. Syst., 2022
Time series anomaly detection in power electronics signals with recurrent and ConvLSTM autoencoders.
Digit. Signal Process., 2022
2021
Deep Learning Based Superconducting Radio-Frequency Cavity Fault Classification at Jefferson Laboratory.
Frontiers Artif. Intell., 2021
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results.
CoRR, 2021
Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information.
CoRR, 2021
Proceedings of the NeurIPS 2022 Competition Track, 2021
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2021
2020
Deep Cellular Recurrent Neural Architecture for Efficient Multidimensional Time-Series Data Processing.
PhD thesis, 2020
Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory.
CoRR, 2020
Deep learning with context encoding for semantic brain tumor segmentation and patient survival prediction.
Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020
2019
Proceedings of the International Joint Conference on Neural Networks, 2019
Multimodal Brain Tumor Segmentation and Survival Prediction Using Hybrid Machine Learning.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2019
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2019
2018
Sparse Simultaneous Recurrent Deep Learning for Robust Facial Expression Recognition.
IEEE Trans. Neural Networks Learn. Syst., 2018
Novel deep generative simultaneous recurrent model for efficient representation learning.
Neural Networks, 2018
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2018
Proceedings of the Medical Imaging 2018: Computer-Aided Diagnosis, 2018
Prediction of Spatial Spectrum in Cognitive Radio using Cellular Simultaneous Recurrent Networks.
Proceedings of the 2018 International Joint Conference on Neural Networks, 2018
Proceedings of the 2018 International Joint Conference on Neural Networks, 2018
2017
Convolutional neural network transfer learning for robust face recognition in NAO humanoid robot.
Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2017
Constrained versus unconstrained learning in generalized recurrent network for image processing.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017
2016
Proceedings of the 2016 International Joint Conference on Neural Networks, 2016
Efficient feature extraction with simultaneous recurrent network for metric learning.
Proceedings of the 2016 International Joint Conference on Neural Networks, 2016
2015
Novel hierarchical Cellular Simultaneous Recurrent neural Network for object detection.
Proceedings of the 2015 International Joint Conference on Neural Networks, 2015
2014
Proceedings of the 2014 International Joint Conference on Neural Networks, 2014