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ORCIDGavneet Singh Chadha
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Books and Theses
- 2024
[b1]Gavneet Singh Chadha:
Deep Neural Network Architectures for Prognostics and Health Management of Industrial Systems. Essen University, Germany, 2024
Journal Articles
- 2023
- [j6]Mohammed Sharafath Abdul Hameed
, Gavneet Singh Chadha, Andreas Schwung, Steven X. Ding:
Gradient Monitored Reinforcement Learning. IEEE Trans. Neural Networks Learn. Syst. 34(8): 4106-4119 (2023) - 2022
- [j5]Gavneet Singh Chadha, Sayed Rafay Bin Shah, Andreas Schwung, Steven X. Ding:
Shared Temporal Attention Transformer for Remaining Useful Lifetime Estimation. IEEE Access 10: 74244-74258 (2022) - [j4]Johannes Pöppelbaum, Gavneet Singh Chadha, Andreas Schwung:
Contrastive learning based self-supervised time-series analysis. Appl. Soft Comput. 117: 108397 (2022) - 2021
- [j3]Gavneet Singh Chadha, Utkarsh Panara, Andreas Schwung, Steven X. Ding:
Generalized dilation convolutional neural networks for remaining useful lifetime estimation. Neurocomputing 452: 182-199 (2021) - [j2]Sayed Rafay Bin Shah, Gavneet Singh Chadha, Andreas Schwung, Steven X. Ding:
A Sequence-to-Sequence Approach for Remaining Useful Lifetime Estimation Using Attention-augmented Bidirectional LSTM. Intell. Syst. Appl. 10-11: 200049 (2021) - [j1]Gavneet Singh Chadha, Intekhab Islam, Andreas Schwung, Steven X. Ding:
Deep Convolutional Clustering-Based Time Series Anomaly Detection. Sensors 21(16): 5488 (2021)
Conference and Workshop Papers
- 2021
- [c10]Gavneet Singh Chadha, Jan Niclas Reimann, Andreas Schwung:
Generalized Dilation Structures in Convolutional Neural Networks. ICPRAM 2021: 79-88 - 2020
- [c9]Gavneet Singh Chadha, S. M. Nazmus Sakeib, Andreas Schwung:
Remaining Useful Lifetime Estimation with Sobolev Training. ETFA 2020: 1447-1450 - [c8]Gavneet Singh Chadha, Jinwoo Kim, Andreas Schwung, Steven X. Ding:
Permutation Learning in Convolutional Neural Networks for Time-Series Analysis. ICANN (1) 2020: 220-231 - 2019
- [c7]Gavneet Singh Chadha, Monica Krishnamoorthy, Andreas Schwung:
Time Series based Fault Detection in Industrial Processes using Convolutional Neural Networks. IECON 2019: 173-178 - [c6]Gavneet Singh Chadha, Arfyan Rabbani, Andreas Schwung:
Comparison of Semi-supervised Deep Neural Networks for Anomaly Detection in Industrial Processes. INDIN 2019: 214-219 - [c5]Gavneet Singh Chadha, Elnaz Meydani, Andreas Schwung:
Regularizing Neural Networks with Gradient Monitoring. INNSBDDL 2019: 196-205 - [c4]Philipp Rehlaender, Maik Schroeer, Gavneet Singh Chadha, Andreas Schwung:
Traffic Sign Detection Using R-CNN. INNSBDDL 2019: 226-235 - 2018
- [c3]Gavneet Singh Chadha, Fabian Westbrink, Thomas Schütte, Andreas Schwung:
Optimal dosing of bulk material using mass-flow estimation and DEM simulation. ICIT 2018: 256-261 - [c2]Fabian Westbrink, Gavneet Singh Chadha, Andreas Schwung:
Integrated IPC for data-driven fault detection. ICPS 2018: 277-282 - 2017
- [c1]Gavneet Singh Chadha, Andreas Schwung:
Comparison of deep neural network architectures for fault detection in Tennessee Eastman process. ETFA 2017: 1-8
Informal and Other Publications
- 2020
- [i3]Mohammed Sharafath Abdul Hameed, Gavneet Singh Chadha, Andreas Schwung, Steven X. Ding:
Gradient Monitored Reinforcement Learning. CoRR abs/2005.12108 (2020) - 2019
- [i2]Gavneet Singh Chadha, Jan Niclas Reimann, Andreas Schwung:
Generalized Dilation Neural Networks. CoRR abs/1905.02961 (2019) - [i1]Gavneet Singh Chadha, Andreas Schwung:
Learning the Non-linearity in Convolutional Neural Networks. CoRR abs/1905.12337 (2019)
Coauthor Index
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