Check computer science papers: citations, claims and retractions

Contrastive Code Representation Learning

Jain, P., Jain, A., Zhang, T., Abbeel, P., Gonzalez, J. E., & Stoica, I. (2020) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 129

Defining AI in Policy versus Practice

Krafft, P. M., Young, M., Katell, M., Huang, K., & Bugingo, G. (2020) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

Ethics and Social Impacts of AIDigitalization, Law, and Regulation

Cited by 129

An Introduction to Quantum Error Correction and Fault-Tolerant Quantum Computation

Gottesman, D. (2009) · arXiv

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 129

Blockchain protocols in clinical trials: Transparency and traceability of consent

Benchoufi, M., Porcher, R., & Ravaud, P. (2018) · F1000Research

Blockchain Technology Applications and SecurityEthics in Clinical Research

Cited by 129

Planning to Explore via Self-Supervised World Models

Sekar, R., Rybkin, O., Daniilidis, K., Abbeel, P., Hafner, D., & Pathak, D. (2020) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 129

A systematic review of federated learning applications for biomedical data

Crowson, M. G., Moukheiber, D., Arévalo, A. R., Lam, B. D., Mantena, S., Rana, A., Goss, D., Bates, D. W., & Celi, L. A. (2022) · PLOS Digital Health

Privacy-Preserving Technologies in DataArtificial Intelligence in Healthcare and Education

Cited by 129

Sentiment Analysis for Software Engineering: How Far Can Pre-trained Transformer Models Go?

Zhang, T., Xu, B., Thung, F., Haryono, S. A., Lo, D., & Jiang, L. (2020) · 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME)

Sentiment Analysis and Opinion MiningSoftware Engineering Research

Cited by 129

Crowdsourcing for Cognitive Science – The Utility of Smartphones

Brown, H. R., Zeidman, P., Smittenaar, P., Adams, R. A., McNab, F., Rutledge, R. B., & Dolan, R. J. (2014) · PLoS ONE

Mobile Crowdsensing and CrowdsourcingOpen Source Software Innovations

Cited by 129

Exploring Visual Prompts for Adapting Large-Scale Models

Bahng, H., Jahanian, A., Sankaranarayanan, S., & Isola, P. (2022) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 129

LambdaNetworks: Modeling Long-Range Interactions Without Attention

Bello, I. (2021) · arXiv

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 129

A survey on medical explainable AI (XAI): recent progress, explainability approach, human interaction and scoring system

Sheu, R.-K., & Pardeshi, M. S. (2022) · Sensors

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

Cited by 129

Machine Learning in IoT Security: Current Solutions and Future Challenges

Hussain, F., Hussain, R., Hassan, S. A., & Hossain, E. (2020) · IEEE Communications Surveys & Tutorials

Network Security and Intrusion DetectionInternet of Things and AI

Cited by 129

Log Parsing with Prompt-based Few-shot Learning

Le, V.-H., & Zhang, H. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)

Software System Performance and ReliabilitySoftware Engineering Research

Cited by 129

Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems

(2018)

Teaching and Learning ProgrammingYouth Development and Social Support

Cited by 129

Quarantined! Examining the Effects of a Community-Wide Moderation Intervention on Reddit

Chandrasekharan, E., Jhaver, S., Bruckman, A., & Gilbert, E. (2022) · ACM Transactions on Computer-Human Interaction

Hate Speech and Cyberbullying DetectionMisinformation and Its Impacts

Cited by 129

Automation: how much is too much?

Hancock, P. A. (2014) · Ergonomics

Ethics and Social Impacts of AIHuman-Automation Interaction and Safety

Cited by 129

Language Models and Cognitive Automation for Economic Research

Korinek, A. (2023) · National Bureau of Economic Research

Artificial Intelligence ApplicationsComputational and Text Analysis Methods

Cited by 129

Requirements-driven test generation for autonomous vehicles with machine learning components

Tuncali, C. E., Fainekos, G., Prokhorov, D., Ito, H., & Kapinski, J. (2020) · IEEE Transactions on Intelligent Vehicles

Software Testing and Debugging TechniquesFormal Methods in Verification

Cited by 129

Exploring the Influence of Identifier Names on Code Quality: An Empirical Study

Butler, S., Wermelinger, M., Yu, Y., & Sharp, H. (2010) · 2010 14th European Conference on Software Maintenance and Reengineering

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 129

Markov decision processes with delays and asynchronous cost collection

Katsikopoulos, K. V., & Engelbrecht, S. E. (2003) · IEEE Transactions on Automatic Control

Formal Methods in VerificationAdaptive Dynamic Programming Control

Cited by 129

An Ontology-Based Cybersecurity Framework for the Internet of Things

Mozzaquatro, B. A., Agostinho, C., Goncalves, D., Martins, J., & Jardim-Goncalves, R. (2018) · Sensors

IoT and Edge/Fog ComputingSemantic Web and Ontologies

Cited by 129

A Theory for Emergence of Complex Skills in Language Models

Arora, S., & Goyal, A. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 129

A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models

Tonmoy, S. M. T. I., Zaman, S. M. M., Jain, V., Rani, A., Rawte, V., Chadha, A., & Das, A. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 129

Understanding and Mitigating Worker Biases in the Crowdsourced Collection of Subjective Judgments

Hube, C., Fetahu, B., & Gadiraju, U. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

Mobile Crowdsensing and CrowdsourcingSentiment Analysis and Opinion Mining

Cited by 129