| Long Short Term Memory Networks for Anomaly Detection in Time Series. P Malhotra, L Vig, G Shroff, P Agarwal Esann 2015, 89, 2015 | 1594 | 2015 |
| LSTM-based encoder-decoder for multi-sensor anomaly detection P Malhotra, A Ramakrishnan, G Anand, L Vig, P Agarwal, G Shroff arXiv preprint arXiv:1607.00148, 2016 | 1125 | 2016 |
| Stabilization of unstable procedures: the recursive projection method GM Shroff, HB Keller SIAM Journal on numerical analysis 30 (4), 1099-1120, 1993 | 328 | 1993 |
| 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 arXiv preprint arXiv:1608.06154, 2016 | 245 | 2016 |
| TimeNet: Pre-trained deep recurrent neural network for time series classification P Malhotra, V TV, L Vig, P Agarwal, G Shroff arXiv preprint arXiv:1706.08838, 2017 | 190 | 2017 |
| 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 arXiv preprint arXiv:1709.01073, 2017 | 179 | 2017 |
| Enterprise cloud computing: technology, architecture, applications G Shroff Cambridge university press, 2010 | 156 | 2010 |
| On updating signal subspaces CH Bischof, GM Shroff IEEE Transactions on Signal Processing 40 (1), 96-105, 1992 | 152 | 1992 |
| Acquiring competitive intelligence from social media L Dey, SM Haque, A Khurdiya, G Shroff Proceedings of the 2011 joint workshop on multilingual OCR and analytics for …, 2011 | 119 | 2011 |
| 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 | 109 | 2019 |
| 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 | 89 | 2019 |
| 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 Proceedings of the acm india joint international conference on data science …, 2018 | 83 | 2018 |
| Catching the long-tail: Extracting local news events from twitter P Agarwal, R Vaithiyanathan, S Sharma, G Shroff Proceedings of The International AAAI Conference on Web and Social Media 6 …, 2012 | 81 | 2012 |
| On the convergence of the cyclic Jacobi method for parallel block orderings G Shroff, R Schreiber SIAM journal on matrix analysis and applications 10 (3), 326-346, 1989 | 74 | 1989 |
| The Intelligent Web: Search, smart algorithms, and big data G Shroff OUP Oxford, 2013 | 73 | 2013 |
| Dynamics of software maintenance P Bhatt, G Shroff, AK Misra ACM SIGSOFT Software Engineering Notes 29 (5), 1-5, 2004 | 70 | 2004 |
| Anomaly detection system and method P Malhotra, G Shroff, P Agarwal, L Vig US Patent 10,223,403, 2019 | 67 | 2019 |
| LSTM-based encoder-decoder for multi-sensor anomaly detection. arXiv 2016 P Malhotra, A Ramakrishnan, G Anand, L Vig, P Agarwal, G Shroff arXiv preprint arXiv:1607.00148, 2016 | 61 | 2016 |
| Transfer learning for clinical time series analysis using deep neural networks P Gupta, P Malhotra, J Narwariya, L Vig, G Shroff Journal of Healthcare Informatics Research 4 (2), 112-137, 2020 | 58 | 2020 |
| Meta-learning for few-shot time series classification J Narwariya, P Malhotra, L Vig, G Shroff, TV Vishnu Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, 28-36, 2020 | 47 | 2020 |
Lovekesh VigTCS ResearchVerified email at tcs.com
