Check computer science papers: citations, claims and retractions

Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review

Zhang, J., Yin, Z., Chen, P., & Nichele, S. (2020) · Information Fusion

Emotion and Mood RecognitionEEG and Brain-Computer Interfaces

Cited by 806

OntoNotes: the 90% solution

Hovy, E., Marcus, M., Palmer, M., Ramshaw, L., & Weischedel, R. (2006) · Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers on XX - NAACL '06

Natural Language Processing TechniquesTopic Modeling

Cited by 806

Black-box Adversarial Attacks with Limited Queries and Information

Ilyas, A., Engstrom, L., Athalye, A., & Lin, J. (2018) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 806

To prune, or not to prune: exploring the efficacy of pruning for model compression

Zhu, M., & Gupta, S. (2017) · arXiv

Advanced Neural Network ApplicationsMachine Learning and Data Classification

Cited by 805

Human Pose Estimation with Iterative Error Feedback

Carreira, J., Agrawal, P., Fragkiadaki, K., & Malik, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Human Pose and Action RecognitionAdvanced Neural Network Applications

Cited by 805

Efficiently Modeling Long Sequences with Structured State Spaces

Gu, A., Goel, K., & Ré, C. (2021) · arXiv

Topic ModelingAdvanced Neural Network Applications

Cited by 805

Constraint-based automatic test data generation

DeMilli, R. A., & Offutt, A. J. (1991) · IEEE Transactions on Software Engineering

Software Testing and Debugging TechniquesVLSI and Analog Circuit Testing

Cited by 804

Constrained Policy Optimization

Achiam, J., Held, D., Tamar, A., & Abbeel, P. (2017) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 804

Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Yue, Y., Chen, Z., Lu, R., Zhao, A., Wang, Z., Yue, Y., Song, S., & Huang, G. (2025) · arXiv

Topic ModelingReinforcement Learning in Robotics

Cited by 804

Geometric Loss Functions for Camera Pose Regression with Deep Learning

Kendall, A., & Cipolla, R. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Robotics and Sensor-Based LocalizationAdvanced Image and Video Retrieval Techniques

Cited by 803

Universal intelligence: a definition of machine intelligence

Legg, S., & Hutter, M. (2007) · Minds and Machines

Psychiatry, Mental Health, NeuroscienceArtificial Intelligence Applications

Cited by 803

Stimulating illusory own-body perceptions

Blanke, O., Ortigue, S., Landis, T., & Seeck, M. (2002) · Nature

Virtual Reality Applications and ImpactsPsychosomatic Disorders and Their Treatments

Cited by 802

What is data justice? The case for connecting digital rights and freedoms globally

Taylor, L. (2017) · Big Data & Society

Ethics and Social Impacts of AIDigitalization, Law, and Regulation

Cited by 802

An Evaluation of Popular Copy-Move Forgery Detection Approaches

Christlein, V., Riess, C., Jordan, J., Riess, C., & Angelopoulou, E. (2012) · IEEE Transactions on Information Forensics and Security

Digital Media Forensic DetectionAdvanced Steganography and Watermarking Techniques

Cited by 802

The EM Algorithm—an Old Folk-song Sung to a Fast New Tune

Meng, X.-L., & Van Dyk, D. (1997) · Journal of the Royal Statistical Society Series B: Statistical Methodology

Bayesian Methods and Mixture ModelsMarkov Chains and Monte Carlo Methods

Cited by 802

Segment anything model for medical image analysis: An experimental study

Mazurowski, M. A., Dong, H., Gu, H., Yang, J., Konz, N., & Zhang, Y. (2023) · Medical Image Analysis

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 802

Recent advances in deep learning for speech research at Microsoft

Deng, L., Li, J., Huang, J.-T., Yao, K., Yu, D., Seide, F., Seltzer, M., Zweig, G., He, X., Williams, J., et al. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 801

Object Detection via a Multi-region and Semantic Segmentation-Aware CNN Model

Gidaris, S., & Komodakis, N. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 801

Artificial Intelligence and Black‐Box Medical Decisions: Accuracy versus Explainability

London, A. J. (2019) · Hastings Center Report

Explainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare and Education

Cited by 801

Deep Audio-Visual Speech Recognition

Afouras, T., Chung, J. S., Senior, A., Vinyals, O., & Zisserman, A. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Speech and Audio ProcessingSpeech Recognition and Synthesis

Cited by 801

Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation

Díaz-Rodríguez, N., Del Ser, J., Coeckelbergh, M., López de Prado, M., Herrera-Viedma, E., & Herrera, F. (2023) · Information Fusion

Ethics and Social Impacts of AIDigitalization, Law, and Regulation

Cited by 800

CNN-SLAM: Real-Time Dense Monocular SLAM with Learned Depth Prediction

Tateno, K., Tombari, F., Laina, I., & Navab, N. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Robotics and Sensor-Based LocalizationAdvanced Vision and Imaging

Cited by 800

Learning and evaluating classifiers under sample selection bias

Zadrozny, B. (2004) · Twenty-first international conference on Machine learning - ICML '04

Machine Learning and Data ClassificationImbalanced Data Classification Techniques

Cited by 800

Network Slicing Based 5G and Future Mobile Networks: Mobility, Resource Management, and Challenges

Zhang, H., Liu, N., Chu, X., Long, K., Aghvami, A.-H., & Leung, V. C. M. (2017) · IEEE Communications Magazine

Software-Defined Networks and 5GAdvanced Wireless Network Optimization

Cited by 800