Check computer science papers: citations, claims and retractions

Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?

Khorrami, P., Paine, T. L., & Huang, T. S. (2015) · 2015 IEEE International Conference on Computer Vision Workshop (ICCVW)

Emotion and Mood RecognitionFace recognition and analysis

Cited by 285

A Comprehensive Survey of Data Mining-based Fraud Detection Research

Phua, C., Lee, V., Smith, K., & Gayler, R. (2010) · arXiv

Imbalanced Data Classification TechniquesBenford’s Law and Fraud Detection

Cited by 285

Metaverse in Healthcare Integrated with Explainable AI and Blockchain: Enabling Immersiveness, Ensuring Trust, and Providing Patient Data Security

Ali, S., Abdullah, Armand, T. P. T., Athar, A., Hussain, A., Ali, M., Yaseen, M., Joo, M.-I., & Kim, H.-C. (2023) · Sensors

Blockchain Technology Applications and SecurityArtificial Intelligence in Healthcare and Education

Cited by 285

Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches

Hussein, S., Kandel, P., Bolan, C. W., Wallace, M. B., & Bagci, U. (2019) · IEEE Transactions on Medical Imaging

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 285

On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trial

Salvi, F., Ribeiro, M. H., Gallotti, R., & West, R. (2024) · Nature Human Behaviour

Ethics and Social Impacts of AIPsychology of Social Influence

Cited by 285

Scaffolding Deep Comprehension Strategies Through Point&Query, AutoTutor, and iSTART

Graesser, A. C., McNamara, D. S., & VanLehn, K. (2005) · Educational Psychologist

Intelligent Tutoring Systems and Adaptive LearningInnovative Teaching and Learning Methods

Cited by 285

Augmented Neural ODEs

Dupont, E., Doucet, A., & Teh, Y. W. (2019) · arXiv

Neural Networks and ApplicationsModel Reduction and Neural Networks

Cited by 285

Automatic 3D liver location and segmentation via convolutional neural network and graph cut

Lu, F., Wu, F., Hu, P., Peng, Z., & Kong, D. (2017) · International Journal of Computer Assisted Radiology and Surgery

Medical Image Segmentation TechniquesMedical Imaging and Analysis

Cited by 284

The Logarithmic Nature of QoE and the Role of the Weber-Fechner Law in QoE Assessment

Reichl, P., Egger, S., Schatz, R., & D'Alconzo, A. (2010) · 2010 IEEE International Conference on Communications

Image and Video Quality AssessmentNetwork Traffic and Congestion Control

Cited by 284

Updates in Human-AI Teams: Understanding and Addressing the Performance/Compatibility Tradeoff

Bansal, G., Nushi, B., Kamar, E., Weld, D. S., Lasecki, W. S., & Horvitz, E. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence

Explainable Artificial Intelligence (XAI)Human-Automation Interaction and Safety

Cited by 284

Duration and Exposure to Virtual Environments: Sickness Curves During and Across Sessions

Kennedy, R. S., Stanney, K. M., & Dunlap, W. P. (2000) · Presence: Teleoperators and Virtual Environments

Virtual Reality Applications and ImpactsSimulation and Modeling Applications

Cited by 284

RobustBench: a standardized adversarial robustness benchmark

Croce, F., Andriushchenko, M., Sehwag, V., Debenedetti, E., Flammarion, N., Chiang, M., Mittal, P., & Hein, M. (2020) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 284

A mathematical theory of semantic development in deep neural networks

Saxe, A. M., McClelland, J. L., & Ganguli, S. (2019) · Proceedings of the National Academy of Sciences

Neural Networks and ApplicationsCategorization, perception, and language

Cited by 284

The adverse effects of code duplication in machine learning models of code

Allamanis, M. (2019) · Proceedings of the 2019 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 284

LSDA: Large Scale Detection Through Adaptation

Hoffman, J., Guadarrama, S., Tzeng, E., Hu, R., Donahue, J., Girshick, R., Darrell, T., & Saenko, K. (2014) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 284

Gemma: Open Models Based on Gemini Research and Technology

Gemma Team, Mesnard, T., Hardin, C., Dadashi, R., Bhupatiraju, S., Pathak, S., Sifre, L., Rivière, M., Kale, M. S., Love, J., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 284

Population Based Training of Neural Networks

Jaderberg, M., Dalibard, V., Osindero, S., Czarnecki, W. M., Donahue, J., Razavi, A., Vinyals, O., Green, T., Dunning, I., Simonyan, K., et al. (2017) · arXiv

Machine Learning and Data ClassificationStochastic Gradient Optimization Techniques

Cited by 284

Standards for security categorization of federal information and information systems

National Institute of Standards and Technology (US) (2004)

Information and Cyber SecurityCybersecurity and Information Systems

Cited by 284

Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets

Geva, M., Goldberg, Y., & Berant, J. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Natural Language Processing TechniquesTopic Modeling

Cited by 284

Human expression recognition from motion using a radial basis function network architecture

Rosenblum, M., Yacoob, Y., & Davis, L. S. (1996) · IEEE Transactions on Neural Networks

Emotion and Mood RecognitionFace recognition and analysis

Cited by 284

Variational Quantum Linear Solver

Bravo-Prieto, C., LaRose, R., Cerezo, M., Subasi, Y., Cincio, L., & Coles, P. J. (2019) · arXiv

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 284

A Multimodal Biomedical Foundation Model Trained from Fifteen Million Image–Text Pairs

Zhang, S., Xu, Y., Usuyama, N., Xu, H., Bagga, J., Tinn, R., Preston, S., Rao, R., Wei, M., Valluri, N., et al. (2025) · NEJM AI

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 283

Graphics processing unit (GPU) programming strategies and trends in GPU computing

Brodtkorb, A. R., Hagen, T. R., & Sætra, M. L. (2013) · Journal of Parallel and Distributed Computing

Parallel Computing and Optimization TechniquesComputer Graphics and Visualization Techniques

Cited by 283

Private predictive analysis on encrypted medical data

Bos, J. W., Lauter, K., & Naehrig, M. (2014) · Journal of Biomedical Informatics

Cryptography and Data SecurityCloud Data Security Solutions

Cited by 283