| Long short term memory networks for anomaly detection in time series P Malhotra, L Vig, G Shroff, P Agarwal Proceedings 89 (9), 94, 2015 | 1967 | 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 | 1461 | 2016 |
| Anomaly detection in ECG time signals via deep long short-term memory networks S Chauhan, L Vig 2015 IEEE international conference on data science and advanced analytics …, 2015 | 554 | 2015 |
| Multi-robot coalition formation L Vig, JA Adams IEEE transactions on robotics 22 (4), 637-649, 2006 | 385 | 2006 |
| 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 | 285 | 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 | 247 | 2017 |
| Tablenet: Deep learning model for end-to-end table detection and tabular data extraction from scanned document images SS Paliwal, D Vishwanath, R Rahul, M Sharma, L Vig 2019 international conference on document analysis and recognition (ICDAR …, 2019 | 230 | 2019 |
| 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 | 200 | 2017 |
| 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 | 139 | 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 | 118 | 2018 |
| Crowdsourcing for chromosome segmentation and deep classification M Sharma, O Saha, A Sriraman, R Hebbalaguppe, L Vig, S Karande Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 113 | 2017 |
| Coalition formation: From software agents to robots L Vig, JA Adams Journal of Intelligent and Robotic Systems 50, 85-118, 2007 | 110 | 2007 |
| An efficient end-to-end neural model for handwritten text recognition A Chowdhury, L Vig arXiv preprint arXiv:1807.07965, 2018 | 106 | 2018 |
| Meta-dermdiagnosis: Few-shot skin disease identification using meta-learning K Mahajan, M Sharma, L Vig Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 104 | 2020 |
| 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 | 104 | 2019 |
| Siamese networks for chromosome classification S Jindal, G Gupta, M Yadav, M Sharma, L Vig Proceedings of the IEEE international conference on computer vision …, 2017 | 104 | 2017 |
| A comparison of shallow and deep learning methods for predicting cognitive performance of stroke patients from MRI lesion images S Chauhan, L Vig, M De Filippo De Grazia, M Corbetta, S Ahmad, M Zorzi Frontiers in neuroinformatics 13, 53, 2019 | 92 | 2019 |
| 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 | 91 | 2020 |
| Anomaly detection system and method P Malhotra, G Shroff, P Agarwal, L Vig US Patent 10,223,403, 2019 | 87 | 2019 |
| Market-based multi-robot coalition formation L Vig, JA Adams Distributed Autonomous Robotic Systems 7, 227-236, 2006 | 87 | 2006 |