Check computer science papers: citations, claims and retractions

Probabilistic Model-Agnostic Meta-Learning

Finn, C., Xu, K., & Levine, S. (2018) · arXiv

Domain Adaptation and Few-Shot LearningMachine Learning and Algorithms

Cited by 327

The view from the left

MCBRIDE, C., & MCKINNA, J. (2004) · Journal of Functional Programming

Logic, programming, and type systemsSyntax, Semantics, Linguistic Variation

Cited by 327

Algebraic Theory of Machines. I. Prime Decomposition Theorem for Finite Semigroups and Machines

Krohn, K., & Rhodes, J. (1965) · Transactions of the American Mathematical Society

semigroups and automata theoryFinite Group Theory Research

Cited by 327

A Critical Evaluation of Website Fingerprinting Attacks

Juarez, M., Afroz, S., Acar, G., Diaz, C., & Greenstadt, R. (2014) · Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security

Internet Traffic Analysis and Secure E-votingPrivacy, Security, and Data Protection

Cited by 327

FinGPT: Open-Source Financial Large Language Models

Yang, H., Liu, X.-Y., & Wang, C. D. (2023) · arXiv

Topic ModelingStock Market Forecasting Methods

Cited by 326

Block4forensic: An integrated lightweight blockchain framework for forensics applications of connected vehicles

Cebe, M., Erdin, E., Akkaya, K., Aksu, H., & Uluagac, S. (2018) · IEEE Communications Magazine

Blockchain Technology Applications and SecurityVehicular Ad Hoc Networks (VANETs)

Cited by 326

European Portuguese Validation of the System Usability Scale (SUS)

Martins, A. I., Rosa, A. F., Queirós, A., Silva, A., & Rocha, N. P. (2015) · Procedia Computer Science

Usability and User Interface DesignInnovative Human-Technology Interaction

Cited by 326

Learning Complexity-Aware Cascades for Deep Pedestrian Detection

Cai, Z., Saberian, M., & Vasconcelos, N. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsVideo Surveillance and Tracking Methods

Cited by 326

The microsoft 2016 conversational speech recognition system

Xiong, W., Droppo, J., Huang, X., Seide, F., Seltzer, M., Stolcke, A., Yu, D., & Zweig, G. (2017) · 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 326

Drumming in Immersive Virtual Reality: The Body Shapes the Way We Play

Kilteni, K., Bergstrom, I., & Slater, M. (2013) · IEEE Transactions on Visualization and Computer Graphics

Virtual Reality Applications and ImpactsSocial Robot Interaction and HRI

Cited by 326

DeepVision: Deepfakes Detection Using Human Eye Blinking Pattern

Jung, T., Kim, S., & Kim, K. (2020) · IEEE Access

Digital Media Forensic DetectionBiometric Identification and Security

Cited by 326

Learning latent structure: carving nature at its joints

Gershman, S. J., & Niv, Y. (2010) · Current Opinion in Neurobiology

Reinforcement Learning in RoboticsNeural and Behavioral Psychology Studies

Cited by 326

Prolegomena to any future artificial moral agent

Allen, C., Varner, G., & Zinser, J. (2000) · Journal of Experimental & Theoretical Artificial Intelligence

Ethics and Social Impacts of AIPhilosophical Ethics and Theory

Cited by 326

Large Language Models Are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models

Deng, Y., Xia, C. S., Peng, H., Yang, C., & Zhang, L. (2023) · Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis

Software Testing and Debugging TechniquesSoftware Engineering Research

Cited by 326

Virtual to Real Reinforcement Learning for Autonomous Driving

Pan, X., You, Y., Wang, Z., & Lu, C. (2017) · Procedings of the British Machine Vision Conference 2017

Reinforcement Learning in RoboticsAutonomous Vehicle Technology and Safety

Cited by 326

An Intelligence in Our Image: The Risks of Bias and Errors in Artificial Intelligence

Osoba, O., & Welser, W. (2017) · RAND Corporation eBooks

Ethics and Social Impacts of AIArtificial Intelligence in Law

Cited by 326

Measuring Program Comprehension: A Large-Scale Field Study with Professionals

Xia, X., Bao, L., Lo, D., Xing, Z., Hassan, A. E., & Li, S. (2018) · IEEE Transactions on Software Engineering

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 326

Virtual to Real Reinforcement Learning for Autonomous Driving

Pan, X., You, Y., Wang, Z., & Lu, C. (2017) · arXiv

Reinforcement Learning in RoboticsAutonomous Vehicle Technology and Safety

Cited by 326

Large language models for generative information extraction: a survey

Xu, D., Chen, W., Peng, W., Zhang, C., Xu, T., Zhao, X., Wu, X., Zheng, Y., Wang, Y., & Chen, E. (2024) · Frontiers of Computer Science

Topic ModelingNatural Language Processing Techniques

Cited by 326

The ManyBugs and IntroClass benchmarks for automated repair of C programs

Le Goues, C., Holtschulte, N., Smith, E. K., Brun, Y., Devanbu, P., Forrest, S., & Weimer, W. (2015) · IEEE Transactions on Software Engineering

Software Testing and Debugging TechniquesSoftware Engineering Research

Cited by 326

Predicting hospital admission at emergency department triage using machine learning

Hong, W. S., Haimovich, A. D., & Taylor, R. A. (2018) · PLOS ONE

Machine Learning in HealthcareEmergency and Acute Care Studies

Cited by 326

A survey on intent-based networking

Leivadeas, A., & Falkner, M. (2023) · IEEE Communications Surveys & Tutorials

Software-Defined Networks and 5GAdvanced Optical Network Technologies

Cited by 326

Artificial intelligence and learning analytics in teacher education: A systematic review

Salas-Pilco, S., Xiao, K., & Hu, X. (2022) · Education Sciences

Online Learning and AnalyticsArtificial Intelligence in Education

Cited by 326

Large Language Models: A Survey

Minaee, S., Mikolov, T., Nikzad, N., Chenaghlu, M., Socher, R., Amatriain, X., & Gao, J. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 325