| Long Short Term Memory Networks for Anomaly Detection in Time Series P Malhotra, L Vig, G Shroff, P Agarwal 23rd European Symposium on Artificial Neural Networks, Computational …, 2015 | 758 | 2015 |
| LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection P Malhotra, A Ramakrishnan, G Anand, L Vig, P Agarwal, G Shroff Anomaly Detection Workshop at 33rd International Conference on Machine …, 2016 | 361 | 2016 |
| Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder P Malhotra, V TV, A Ramakrishnan, G Anand, L Vig, P Agarwal, G Shroff 1st ACM SIGKDD Workshop on Machine Learning for Prognostics and Health …, 2016 | 118 | 2016 |
| Predicting Remaining Useful Life Using Time Series Embeddings based on Recurrent Neural Networks N Gugulothu, V TV, P Malhotra, L Vig, P Agarwal, G Shroff International Journal of Prognostics and Health Management, 2018 9 (004), 2018 | 74 | 2018 |
| Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks N Gugulothu, V TV, P Malhotra, L Vig, P Agarwal, G Shroff 2nd ACM SIGKDD Workshop on Machine Learning for Prognostics and Health …, 2017 | 74 | 2017 |
| TimeNet: Pre-trained deep recurrent neural network for time series classification P Malhotra, V TV, L Vig, P Agarwal, G Shroff 25th European Symposium on Artificial Neural Networks, Computational …, 2017 | 70 | 2017 |
| Approximate Incremental Big-Data Harmonization P Agarwal, G Shroff, P Malhotra BigData Congress, 2013 | 38 | 2013 |
| NISER: Normalized item and session representations to handle popularity bias P Gupta, D Garg, P Malhotra, L Vig, G Shroff arXiv preprint arXiv:1909.04276, 2019 | 32* | 2019 |
| Transfer Learning for Clinical Time Series Analysis using Deep Neural Networks P Gupta, P Malhotra, J Narwariya, L Vig, G Shroff | 27 | 2019 |
| Transfer Learning for Clinical Time Series Analysis using Recurrent Neural Networks P Gupta, P Malhotra, L Vig, G Shroff Machine Learning for Medicine and Healthcare Workshop at KDD 2018, 2018 | 27* | 2018 |
| Anomaly detection system and method P Malhotra, G Shroff, P Agarwal, L Vig US Patent 10,223,403, 2019 | 25 | 2019 |
| Graph-Parallel Entity Resolution using LSH & IMM P Malhotra, P Agarwal, G Shroff EDBT/ICDT Workshops, 2014 | 24 | 2014 |
| Online Anomaly Detection with Concept Drift Adaptation using Recurrent Neural Networks S Saurav, P Malhotra, V TV, N Gugulothu, L Vig, P Agarwal, G Shroff CoDS-COMAD '18 Proceedings of the ACM India Joint International Conference …, 2018 | 21 | 2018 |
| Sequence and Time Aware Neighborhood for Session-based Recommendations: STAN D Garg, P Gupta, P Malhotra, L Vig, G Shroff Proceedings of the 42nd International ACM SIGIR Conference on Research and …, 2019 | 20 | 2019 |
| ODE-augmented Training Improves Anomaly Detection in Sensor Data from Machines M Yadav, P Malhotra, L Vig, K Sriram, G Shroff NIPS Time Series Workshop, 2015 | 19 | 2015 |
| ConvTimeNet: A pre-trained deep convolutional neural network for time series classification K Kashiparekh, J Narwariya, P Malhotra, L Vig, G Shroff 2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019 | 18 | 2019 |
| Using Features from Pre-trained TimeNet for Clinical Predictions P Gupta, P Malhotra, L Vig, G Shroff 3rd International Workshop on Knowledge Discovery in Healthcare Data at IJCAI, 2018 | 14 | 2018 |
| Fusing Features based on Signal Properties and TimeNet for Time Series Classification. A Ukil, P Malhotra, S Bandyopadhyay, T Bose, I Sahu, A Mukherjee, L Vig, ... ESANN, 2019 | 7 | 2019 |
| Sparse Neural Networks for Anomaly Detection in High-Dimensional Time Series N Gugulothu, P Malhotra, L Vig, G Shroff AI4IOT Workshop at IJCAI 2018, 2018 | 7 | 2018 |
| Predicting remaining useful life using time series embeddings based on recurrent neural networks VTV Narendhar Gugulothu, P Malhotra, L Vig, P Agarwal, G Shroff International Journal on Prognostics and Health Management, IJPHM, 2017 | 7 | 2017 |
Gautam ShroffSr. Vice President and Head - TCS ResearchVerified email at tcs.com
