Liu, Yan

Recurrent Neural Networks for Multivariate Time Series with Missing Values

Che, Z., Purushotham, S., Cho, K., Sontag, D., & Liu, Y. (2018) · Scientific Reports

Time Series Analysis and ForecastingMachine Learning in Healthcare

Cited by 2,370

CSI: A Hybrid Deep Model for Fake News Detection

Ruchansky, N., Seo, S., & Liu, Y. (2017) · Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

Misinformation and Its ImpactsSpam and Phishing Detection

Cited by 1,039

Combating Fake News: A Survey on Identification and Mitigation Techniques

Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M., & Liu, Y. (2019) · ACM Transactions on Intelligent Systems and Technology

Misinformation and Its ImpactsSpam and Phishing Detection

Cited by 514

Benchmarking deep learning models on large healthcare datasets

Purushotham, S., Meng, C., Che, Z., & Liu, Y. (2018) · Journal of Biomedical Informatics

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 406

TrustLLM: Trustworthiness in Large Language Models

Huang, Y., Sun, L., Wang, H., Wu, S., Zhang, Q., Li, Y., Gao, C., Huang, Y., Lyu, W., Zhang, Y., et al. (2024) · arXiv

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 256

Neural User Response Generator: Fake News Detection with Collective User Intelligence

Qian, F., Gong, C., Sharma, K., & Liu, Y. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

Misinformation and Its ImpactsSpam and Phishing Detection

Cited by 237

Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records

Che, Z., Cheng, Y., Zhai, S., Sun, Z., & Liu, Y. (2017) · 2017 IEEE International Conference on Data Mining (ICDM)

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 162

Interpretable and Trustworthy Deepfake Detection via Dynamic Prototypes

Trinh, L., Tsang, M., Rambhatla, S., & Liu, Y. (2021) · 2021 IEEE Winter Conference on Applications of Computer Vision (WACV)

Digital Media Forensic DetectionExplainable Artificial Intelligence (XAI)

Cited by 115

TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Cao, D., Jia, F., Arik, S. O., Pfister, T., Zheng, Y., Ye, W., & Liu, Y. (2023) · arXiv

Time Series Analysis and ForecastingForecasting Techniques and Applications

Cited by 59

An Examination of Fairness of AI Models for Deepfake Detection

Trinh, L., & Liu, Y. (2021) · Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence

Digital Media Forensic DetectionEthics and Social Impacts of AI

Cited by 44

D$^2$-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios

Che, Z., Li, G., Li, T., Jiang, B., Shi, X., Zhang, X., Lu, Y., Wu, G., Liu, Y., & Ye, J. (2019) · arXiv

Autonomous Vehicle Technology and SafetyAdvanced Neural Network Applications

Cited by 36

Toward Mitigating Misinformation and Social Media Manipulation in LLM Era

Zhang, Y., Sharma, K., Du, L., & Liu, Y. (2024) · Companion Proceedings of the ACM Web Conference 2024

Misinformation and Its ImpactsSpam and Phishing Detection

Cited by 25

MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset

Meng, C., Trinh, L., Xu, N., & Liu, Y. (2021) · Research Square

Machine Learning in HealthcareExplainable Artificial Intelligence (XAI)

Cited by 21

Partially Generative Neural Networks for Gang Crime Classification with Partial Information

Seo, S., Chan, H., Brantingham, P. J., Leap, J., Vayanos, P., Tambe, M., & Liu, Y. (2018) · Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society

Data Mining and Machine Learning ApplicationsCrime Patterns and Interventions

Cited by 17

An Examination on the Effectiveness of Divide-and-Conquer Prompting in Large Language Models

Zhang, Y., Du, L., Cao, D., Fu, Q., & Liu, Y. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 2