Venkatesh, Svetha

Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection

Gong, D., Liu, L., Le, V., Saha, B., Mansour, M. R., Venkatesh, S., & Van Den Hengel, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Anomaly Detection Techniques and ApplicationsAdversarial Robustness in Machine Learning

Cited by 1,930

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

Luo, W., Phung, D., Tran, T., Gupta, S., Rana, S., Karmakar, C., Shilton, A., Yearwood, J., Dimitrova, N., Ho, T. B., et al. (2016) · Journal of Medical Internet Research

Artificial Intelligence in HealthcareMachine Learning in Healthcare

Cited by 988

Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model

Duong, T. V., Bui, H. H., Phung, D. Q., & Venkatesh, S. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)

Context-Aware Activity Recognition SystemsHuman Pose and Action Recognition

Cited by 535

Predicting healthcare trajectories from medical records: a deep learning approach

Pham, T., Tran, T., Phung, D., & Venkatesh, S. (2017) · Journal of Biomedical Informatics

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 447

Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety

Huckvale, K., Venkatesh, S., & Christensen, H. (2019) · npj Digital Medicine

Digital Mental Health InterventionsMental Health Research Topics

Cited by 433

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Pham, T., Tran, T., Phung, D., & Venkatesh, S. (2016) · Lecture Notes in Computer Science

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 269

Hierarchical Conditional Relation Networks for Video Question Answering

Le, T. M., Le, V., Venkatesh, S., & Tran, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsHuman Pose and Action Recognition

Cited by 260

Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)

Tran, T., Nguyen, T. D., Phung, D., & Venkatesh, S. (2015) · Journal of Biomedical Informatics

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 172

Risk stratification using data from electronic medical records better predicts suicide risks than clinician assessments

Tran, T., Luo, W., Phung, D., Harvey, R., Berk, M., Kennedy, R. L., & Venkatesh, S. (2014) · BMC Psychiatry

Suicide and Self-Harm StudiesMachine Learning in Healthcare

Cited by 150

Policy Recognition in the Abstract Hidden Markov Model

Bui, H. H., Venkatesh, S., & West, G. (2002) · Journal of Artificial Intelligence Research

AI-based Problem Solving and PlanningBayesian Modeling and Causal Inference

Cited by 123

Efficient duration and hierarchical modeling for human activity recognition

Duong, T., Phung, D., Bui, H., & Venkatesh, S. (2009) · Artificial Intelligence

Context-Aware Activity Recognition SystemsHuman Pose and Action Recognition

Cited by 115

Recognition of human activity through hierarchical stochastic learning

Luhr, S., Bui, H. H., Venkatesh, S., & West, G. A. W. (2004) · Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003).

Context-Aware Activity Recognition SystemsHuman Pose and Action Recognition

Cited by 58

The Future Proofing Study: Design, methods and baseline characteristics of a prospective cohort study of the mental health of Australian adolescents

Werner‐Seidler, A., Maston, K., Calear, A. L., Batterham, P. J., Larsen, M. E., Torok, M., O’Dea, B., Huckvale, K., Beames, J. R., Brown, L., et al. (2023) · International Journal of Methods in Psychiatric Research

Child and Adolescent Psychosocial and Emotional DevelopmentMental Health Treatment and Access

Cited by 37

Explicit State Duration HMM for Abnormality Detection in Sequences of Human Activity

Lühr, S., Venkatesh, S., West, G., & Bui, H. H. (2004) · Lecture Notes in Computer Science

Context-Aware Activity Recognition SystemsAnomaly Detection Techniques and Applications

Cited by 34

Solving for X: Evidence for sex‐specific autism biomarkers across multiple transcriptomic studies

Lee, S. C., Quinn, T. P., Lai, J., Kong, S. W., Hertz‐Picciotto, I., Glatt, S. J., Crowley, T. M., Venkatesh, S., & Nguyen, T. (2019) · American Journal of Medical Genetics Part B: Neuropsychiatric Genetics

Autism Spectrum Disorder ResearchGenetics and Neurodevelopmental Disorders

Cited by 14

Cost-aware Multi-objective Bayesian optimisation

Abdolshah, M., Shilton, A., Rana, S., Gupta, S., & Venkatesh, S. (2019) · arXiv

Advanced Bandit Algorithms ResearchAdvanced Multi-Objective Optimization Algorithms

Cited by 12

Nonparametric discovery of movement patterns from accelerometer signals

Nguyen, T., Gupta, S., Venkatesh, S., & Phung, D. (2016) · Pattern Recognition Letters

Context-Aware Activity Recognition SystemsPhysical Activity and Health

Cited by 8

Learning People Movement Model from Multiple Cameras for Behaviour Recognition

Nguyen, N. T., Venkatesh, S., West, G. A. W., & Bui, H. H. (2004) · Lecture Notes in Computer Science

Video Surveillance and Tracking MethodsHuman Pose and Action Recognition

Cited by 7