Check computer science papers: citations, claims and retractions

Meta-Learning in Neural Networks: A Survey

Hospedales, T., Antoniou, A., Micaelli, P., & Storkey, A. (2020) · arXiv

Domain Adaptation and Few-Shot LearningMachine Learning and Algorithms

Cited by 886

Meta-Learning in Neural Networks: A Survey

Hospedales, T. M., Antoniou, A., Micaelli, P., & Storkey, A. J. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Domain Adaptation and Few-Shot LearningMachine Learning and Algorithms

Cited by 886

Recommender Systems: An Introduction

Jannach, D., Zanker, M., Felfernig, A., & Friedrich, G. (2010) · Cambridge University Press eBooks

Recommender Systems and TechniquesInformation Retrieval and Search Behavior

Cited by 886

Is object localization for free? - Weakly-supervised learning with convolutional neural networks

Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 886

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Bresson, G., Alsayed, Z., Yu, L., & Glaser, S. (2017) · IEEE Transactions on Intelligent Vehicles

Robotics and Sensor-Based LocalizationAutonomous Vehicle Technology and Safety

Cited by 885

On the Foundations of Relaxation Labeling Processes

Hummel, R. A., & Zucker, S. W. (1983) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Medical Image Segmentation TechniquesDigital Image Processing Techniques

Cited by 885

Correlation Matrix Memories

Kohonen, T. (1972) · IEEE Transactions on Computers

Neural Networks and ApplicationsCognitive Computing and Networks

Cited by 885

Swarm Learning for decentralized and confidential clinical machine learning

Warnat-Herresthal, S., Schultze, H., Shastry, K. L., Manamohan, S., Mukherjee, S., Garg, V., Sarveswara, R., Händler, K., Pickkers, P., Aziz, N. A., et al. (2021) · Nature

Privacy-Preserving Technologies in DataBlockchain Technology Applications and Security

Cited by 884

AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias

Bellamy, R. K. E., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilović, A., et al. (2019) · IBM Journal of Research and Development

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 884

Generalized Out-of-Distribution Detection: A Survey

Yang, J., Zhou, K., Li, Y., & Liu, Z. (2024) · International Journal of Computer Vision

Adversarial Robustness in Machine LearningAnomaly Detection Techniques and Applications

Cited by 884

Simultaneous feature learning and hash coding with deep neural networks

Lai, H., Pan, Y., Liu, Y., & Yan, S. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Image and Video Retrieval TechniquesImage Retrieval and Classification Techniques

Cited by 884

The success of open source

Weber, S. (2004) · Choice Reviews Online

Open Source Software InnovationsDigital Platforms and Economics

Cited by 883

Speech synthesis from neural decoding of spoken sentences

Anumanchipalli, G. K., Chartier, J., & Chang, E. F. (2019) · Nature

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 883

Big Data in Education: The digital future of learning, policy and practice

Williamson, B. (2017) · SAGE Publications Ltd eBooks

Online Learning and AnalyticsDigital Education and Society

Cited by 883

Vega-Lite: A Grammar of Interactive Graphics

Satyanarayan, A., Moritz, D., Wongsuphasawat, K., & Heer, J. (2017) · IEEE Transactions on Visualization and Computer Graphics

Data Visualization and AnalyticsComputer Graphics and Visualization Techniques

Cited by 883

Blockchained On-Device Federated Learning

Kim, H., Park, J., Bennis, M., & Kim, S.-L. (2018) · arXiv

Blockchain Technology Applications and SecurityPrivacy-Preserving Technologies in Data

Cited by 882

Separation of a mixture of independent signals using time delayed correlations

Molgedey, L., & Schuster, H. G. (1994) · Physical Review Letters

Blind Source Separation TechniquesSpeech and Audio Processing

Cited by 882

From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices

Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020) · Science and Engineering Ethics

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 882

YOLOv12: Attention-Centric Real-Time Object Detectors

Tian, Y., Ye, Q., & Doermann, D. (2025) · arXiv

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 882

A review of semantic segmentation using deep neural networks

Guo, Y., Liu, Y., Georgiou, T., & Lew, M. S. (2018) · International Journal of Multimedia Information Retrieval

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 882

SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

Li, B., Wang, R., Wang, G., Ge, Y., Ge, Y., & Shan, Y. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 882

Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning

Wang, Z., Song, M., Zhang, Z., Song, Y., Wang, Q., & Qi, H. (2019) · IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 881

Sharing Detailed Research Data Is Associated with Increased Citation Rate

Piwowar, H. A., Day, R. S., & Fridsma, D. B. (2007) · PLoS ONE

Research Data Management Practicesscientometrics and bibliometrics research

Cited by 881

A decentralized privacy-preserving healthcare blockchain for IoT

Dwivedi, A. D., Srivastava, G., Dhar, S., & Singh, R. (2019) · Sensors

Blockchain Technology Applications and SecurityInternet of Things and AI

Cited by 881