| Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection D Gong, L Liu, V Le, B Saha, MR Mansour, S Venkatesh, A Hengel Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 1690 | 2019 |
| Learning regularity in skeleton trajectories for anomaly detection in videos R Morais, V Le, T Tran, B Saha, M Mansour, S Venkatesh Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 354 | 2019 |
| Comparative antimutagenic and anticlastogenic effects of green tea and black tea: a review S Gupta, B Saha, AK Giri Mutation Research/Reviews in Mutation Research 512 (1), 37-65, 2002 | 255 | 2002 |
| A framework for classifying online mental health-related communities with an interest in depression B Saha, T Nguyen, D Phung, S Venkatesh IEEE journal of biomedical and health informatics 20 (4), 1008-1015, 2016 | 90 | 2016 |
| Anomaly Detection in Large-Scale Data Stream Networks DS Pham, S Venkatesh, M Lazarescu, S Budhaditya Data Mining and Knowledge Discovery, 2012 | 79 | 2012 |
| Hengel Avd (2019) Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection D Gong, L Liu, V Le, B Saha, MR Mansour, S Venkatesh Proceedings of the IEEE/CVF international conference on computer vision …, 0 | 57 | |
| Improved subspace clustering via exploitation of spatial constraints DS Pham, S Budhaditya, D Phung, S Venkatesh 2012 IEEE Conference on computer vision and pattern recognition, 550-557, 2012 | 49 | 2012 |
| Systems and methods for detecting anomalies from data S Venkatesh, B Saha, MM Lazarescu, DS Pham US Patent 8,744,124, 2014 | 47 | 2014 |
| Multiple task transfer learning with small sample sizes B Saha, S Gupta, D Phung, S Venkatesh Knowledge and information systems 46, 315-342, 2016 | 41 | 2016 |
| Effective anomaly detection in sensor networks data streams S Budhaditya, DS Pham, M Lazarescu, S Venkatesh 2009 Ninth IEEE International Conference on Data Mining, 722-727, 2009 | 41 | 2009 |
| Sparse Subspace Clustering via Group Sparse Coding B Saha, D Phung, DS Pham, S Venkatesh SIAM International Conference on Data Mining (SDM), 2013 | 28 | 2013 |
| Infrequent item mining in multiple data streams B Saha, M Lazarescu, S Venkatesh Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007 …, 2007 | 21 | 2007 |
| Effective sparse imputation of patient conditions in electronic medical records for emergency risk predictions B Saha, S Gupta, D Phung, S Venkatesh Knowledge and Information Systems 53, 179-206, 2017 | 15 | 2017 |
| Understanding patient complaint characteristics using contextual clinical BERT embeddings B Saha, S Lisboa, S Ghosh 2020 42nd Annual International Conference of the IEEE Engineering in …, 2020 | 11 | 2020 |
| A framework for mixed-type multioutcome prediction with applications in healthcare B Saha, S Gupta, D Phung, S Venkatesh IEEE journal of biomedical and health informatics 21 (4), 1182-1191, 2017 | 10 | 2017 |
| A new transfer learning framework with application to model-agnostic multi-task learning S Gupta, S Rana, B Saha, D Phung, S Venkatesh Knowledge and Information Systems 49, 933-973, 2016 | 10 | 2016 |
| Detection of cross-channel anomalies from multiple data channels DS Pham, B Saha, DQ Phung, S Venkatesh 2011 IEEE 11th International Conference on Data Mining, 527-536, 2011 | 10 | 2011 |
| Multi-task transfer learning for in-hospital-death prediction of ICU patients C Karmakar, B Saha, M Palaniswami, S Venkatesh 2016 38th Annual International Conference of the IEEE Engineering in …, 2016 | 8 | 2016 |
| Clustering Patient Medical Records via Sparse Subspace Representation B Saha, D Phung, DS Pham, S Venkatesh The 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2013 | 8 | 2013 |
| Scalable network-wide anomaly detection using compressed data D Pham, B Saha, M Lazarescu, S Venkatesh Deakin University, 2009 | 7 | 2009 |
Svetha VenkateshDeakin Distinguished Professor, Deakin UniversityVerified email at deakin.edu.au
