| 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 | 1908 | 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 | 1421 | 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 | 281 | 2016 |
| 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 | 240 | 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 International Journal of Prognostics and Health Management, 2018 9 (004), 2018 | 198 | 2018 |
| 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 | 141* | 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 | 136 | 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 CoDS-COMAD '18 Proceedings of the ACM India Joint International Conference …, 2018 | 114 | 2018 |
| 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 | 111* | 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 | 100 | 2019 |
| Transfer Learning for Clinical Time Series Analysis using Deep Neural Networks P Gupta, P Malhotra, J Narwariya, L Vig, G Shroff | 86 | 2019 |
| Anomaly detection system and method P Malhotra, G Shroff, P Agarwal, L Vig US Patent 10,223,403, 2019 | 80 | 2019 |
| 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 | 52 | 2020 |
| Approximate Incremental Big-Data Harmonization P Agarwal, G Shroff, P Malhotra BigData Congress, 2013 | 46 | 2013 |
| 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 | 37 | 2018 |
| Causer: Causal session-based recommendations for handling popularity bias P Gupta, A Sharma, P Malhotra, L Vig, G Shroff Proceedings of the 30th ACM international conference on information …, 2021 | 36 | 2021 |
| 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 | 32 | 2018 |
| Graph-Parallel Entity Resolution using LSH & IMM P Malhotra, P Agarwal, G Shroff EDBT/ICDT Workshops, 2014 | 32 | 2014 |
| Graph neural networks for leveraging industrial equipment structure: An application to remaining useful life estimation J Narwariya, P Malhotra, V TV, L Vig, G Shroff arXiv preprint arXiv:2006.16556, 2020 | 30 | 2020 |
| 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 | 30 | 2015 |
Gautam ShroffProfessor of Computer Science and EngineeringVerified email at iiitd.ac.in
