Check computer science papers: citations, claims and retractions

Deep Semi-Supervised Anomaly Detection

Ruff, L., Vandermeulen, R. A., Görnitz, N., Binder, A., Müller, E., Müller, K.-R., & Kloft, M. (2019) · arXiv

Anomaly Detection Techniques and ApplicationsAdversarial Robustness in Machine Learning

Cited by 388

Program Synthesis with Large Language Models

Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C., Terry, M., Le, Q., et al. (2021) · arXiv

Software Engineering ResearchTeaching and Learning Programming

Cited by 388

Measuring the VC-Dimension of a Learning Machine

Vapnik, V., Levin, E., & Cun, Y. L. (1994) · Neural Computation

Machine Learning and AlgorithmsNeural Networks and Applications

Cited by 388

Content-based image retrieval in radiology: current status and future directions

Akgül, C. B., Rubin, D. L., Napel, S., Beaulieu, C. F., Greenspan, H., & Acar, B. (2011) · Journal of Digital Imaging

Image Retrieval and Classification TechniquesRadiomics and Machine Learning in Medical Imaging

Cited by 388

Adapting Object Detectors via Selective Cross-Domain Alignment

Zhu, X., Pang, J., Yang, C., Shi, J., & Lin, D. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 387

DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection

Yang, Y., Zhang, C., Zhou, T., Wen, Q., & Sun, L. (2023) · Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Anomaly Detection Techniques and ApplicationsTime Series Analysis and Forecasting

Cited by 387

Augmented Reality in Cultural Heritage: An Overview of the Last Decade of Applications

Boboc, R. G., Băutu, E., Gîrbacia, F., Popovici, N., & Popovici, D.-M. (2022) · Applied Sciences

Augmented Reality Applications3D Surveying and Cultural Heritage

Cited by 387

Reducing Transformer Depth on Demand with Structured Dropout

Fan, A., Grave, E., & Joulin, A. (2019) · arXiv

Topic ModelingAdvanced Neural Network Applications

Cited by 387

Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks

Hasson, U., Nastase, S. A., & Goldstein, A. (2020) · Neuron

Neural Networks and ApplicationsEmbodied and Extended Cognition

Cited by 387

Quality Assessment of In-the-Wild Videos

Li, D., Jiang, T., & Jiang, M. (2019) · Proceedings of the 27th ACM International Conference on Multimedia

Image and Video Quality AssessmentDigital Media Forensic Detection

Cited by 387

Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatics

Lu, Y., Huang, X., Dai, Y., Maharjan, S., & Zhang, Y. (2020) · IEEE Transactions on Industrial Informatics

Privacy-Preserving Technologies in DataIoT and Edge/Fog Computing

Cited by 387

Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity

Möller, J., Trilling, D., Helberger, N., & van Es, B. (2018) · Information, Communication & Society

Recommender Systems and TechniquesMedia Influence and Politics

Cited by 387

Addressing the class imbalance problem in medical datasets

Rahman, M. M., & Davis, D. N. (2013) · International Journal of Machine Learning and Computing

Imbalanced Data Classification TechniquesMachine Learning and Data Classification

Cited by 387

Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course

Ouyang, F., Wu, M., Zheng, L., Zhang, L., & Jiao, P. (2023) · International Journal of Educational Technology in Higher Education

Online Learning and AnalyticsIntelligent Tutoring Systems and Adaptive Learning

Cited by 387

Vehicle Type Classification Using a Semisupervised Convolutional Neural Network

Dong, Z., Wu, Y., Pei, M., & Jia, Y. (2015) · IEEE Transactions on Intelligent Transportation Systems

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 387

Graph networks as learnable physics engines for inference and control

Sanchez-Gonzalez, A., Heess, N., Springenberg, J. T., Merel, J., Riedmiller, M., Hadsell, R., & Battaglia, P. (2018) · arXiv

Reinforcement Learning in RoboticsModel Reduction and Neural Networks

Cited by 387

AI-based personalized E-learning systems: Issues, challenges, and solutions

Murtaza, M., Ahmed, Y., Shamsi, J. A., Sherwani, F., & Usman, M. (2022) · IEEE Access

Intelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in Education

Cited by 386

New theoretical approaches for human‐computer interaction

Rogers, Y. (2004) · Annual Review of Information Science and Technology

Innovative Human-Technology InteractionUsability and User Interface Design

Cited by 386

Toward an Affect-Sensitive AutoTutor

D'Mello, S., Picard, R. W., & Graesser, A. (2007) · IEEE Intelligent Systems

Intelligent Tutoring Systems and Adaptive LearningEmotion and Mood Recognition

Cited by 386

Efficient Video Object Segmentation via Network Modulation

Yang, L., Wang, Y., Xiong, X., Yang, J., & Katsaggelos, A. K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 386

Benefits of taking a virtual field trip in immersive virtual reality: Evidence for the immersion principle in multimedia learning

Makransky, G., & Mayer, R. E. (2022) · Educational Psychology Review

Virtual Reality Applications and ImpactsVisual and Cognitive Learning Processes

Cited by 386

Object Detection from Video Tubelets with Convolutional Neural Networks

Kang, K., Ouyang, W., Li, H., & Wang, X. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsVideo Surveillance and Tracking Methods

Cited by 386

Framework for Improving Critical Infrastructure Cybersecurity, Version 1.1

National Institute of Standards and Technology (2018)

Information and Cyber SecurityCybersecurity and Cyber Warfare Studies

Cited by 386

PhysioNet: a Web-based resource for the study of physiologic signals

Moody, G. B., Mark, R. G., & Goldberger, A. L. (2001) · IEEE Engineering in Medicine and Biology Magazine

Research Data Management PracticesElectronic Health Records Systems

Cited by 386