Check computer science papers: citations, claims and retractions

ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

Salem, A., Zhang, Y., Humbert, M., Berrang, P., Fritz, M., & Backes, M. (2018) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 873

Predictably Unequal? The Effects of Machine Learning on Credit Markets

FUSTER, A., GOLDSMITH‐PINKHAM, P., RAMADORAI, T., & WALTHER, A. (2022) · The Journal of Finance

Ethics and Social Impacts of AIFinancial Distress and Bankruptcy Prediction

Cited by 872

The go-go interaction technique: non-linear mapping for direct manipulation in VR

Poupyrev, I., Billinghurst, M., Weghorst, S., & Ichikawa, T. (1996) · Proceedings of the 9th annual ACM symposium on User interface software and technology - UIST '96

Interactive and Immersive DisplaysInnovative Human-Technology Interaction

Cited by 872

Perceptual load as a major determinant of the locus of selection in visual attention

Lavie, N., & Tsal, Y. (1994) · Perception & Psychophysics

Visual Attention and Saliency DetectionVisual perception and processing mechanisms

Cited by 870

Data mining in education

Romero, C., & Ventura, S. (2013) · WIREs Data Mining and Knowledge Discovery

Online Learning and AnalyticsIntelligent Tutoring Systems and Adaptive Learning

Cited by 869

End-to-end Neural Coreference Resolution

Lee, K., He, L., Lewis, M., & Zettlemoyer, L. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Natural Language Processing TechniquesTopic Modeling

Cited by 869

Is ChatGPT a General-Purpose Natural Language Processing Task Solver?

Qin, C., Zhang, A., Zhang, Z., Chen, J., Yasunaga, M., & Yang, D. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 869

A review on sentiment analysis and emotion detection from text

Nandwani, P., & Verma, R. (2021) · Social Network Analysis and Mining

Sentiment Analysis and Opinion MiningEmotion and Mood Recognition

Cited by 869

Future Edge Cloud and Edge Computing for Internet of Things Applications

Pan, J., & McElhannon, J. (2018) · IEEE Internet of Things Journal

IoT and Edge/Fog ComputingSoftware-Defined Networks and 5G

Cited by 868

‘Hypernudge’: big Data as a mode of regulation by design

Yeung, K. (2017) · Information, Communication & Society

Ethics and Social Impacts of AIDigitalization, Law, and Regulation

Cited by 868

3-D sound for virtual reality and multimedia

Madole, D., & Begault, D. (1995) · Computer Music Journal

Speech and Audio ProcessingMusic Technology and Sound Studies

Cited by 868

Needs, affect, and interactive products – Facets of user experience

Hassenzahl, M., Diefenbach, S., & Göritz, A. (2010) · Interacting with Computers

Innovative Human-Technology InteractionUsability and User Interface Design

Cited by 867

Linformer: Self-Attention with Linear Complexity

Wang, S., Li, B. Z., Khabsa, M., Fang, H., & Ma, H. (2020) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 867

Theory of Quantum Error Correction for General Noise

Knill, E., Laflamme, R., & Viola, L. (2000) · Physical Review Letters

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 866

Ensemble of exemplar-SVMs for object detection and beyond

Malisiewicz, T., Gupta, A., & Efros, A. A. (2011) · 2011 International Conference on Computer Vision

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 865

Trust in recommender systems

O'Donovan, J., & Smyth, B. (2005) · Proceedings of the 10th international conference on Intelligent user interfaces

Recommender Systems and TechniquesAccess Control and Trust

Cited by 865

Trained Ternary Quantization

Zhu, C., Han, S., Mao, H., & Dally, W. J. (2016) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 865

Unified Vision-Language Pre-Training for Image Captioning and VQA

Zhou, L., Palangi, H., Zhang, L., Hu, H., Corso, J., & Gao, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 864

Pinocchio: Nearly Practical Verifiable Computation

Parno, B., Howell, J., Gentry, C., & Raykova, M. (2013) · 2013 IEEE Symposium on Security and Privacy

Cryptography and Data SecurityCloud Data Security Solutions

Cited by 864

A survey on unsupervised outlier detection in high‐dimensional numerical data

Zimek, A., Schubert, E., & Kriegel, H. (2012) · Statistical Analysis and Data Mining The ASA Data Science Journal

Anomaly Detection Techniques and ApplicationsAdvanced Statistical Methods and Models

Cited by 864

Explainable Artificial Intelligence in education

Khosravi, H., Shum, S. B., Chen, G., Conati, C., Tsai, Y.-S., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S., & Gašević, D. (2022) · Computers and Education: Artificial Intelligence

Explainable Artificial Intelligence (XAI)Artificial Intelligence in Education

Cited by 863

ImageBind One Embedding Space to Bind Them All

Girdhar, R., El-Nouby, A., Liu, Z., Singh, M., Alwala, K. V., Joulin, A., & Misra, I. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 863

The Creation and Detection of Deepfakes: A Survey

Mirsky, Y., & Lee, W. (2022) · ACM Computing Surveys

Digital Media Forensic DetectionGenerative Adversarial Networks and Image Synthesis

Cited by 863

Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications

Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014) · International Journal of Production Economics

Data Quality and ManagementBig Data and Business Intelligence

Cited by 863