Check computer science papers: citations, claims and retractions

Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming

Mozannar, H., Bansal, G., Fourney, A., & Horvitz, E. (2022) · arXiv

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 114

‘Scaling molecular dynamics beyond 100, 000 processor cores for large-scale biophysical simulations’

Jung, J., Nishima, W., Daniels, M., Bascom, G., Kobayashi, C., Adedoyin, A., Wall, M., Lappala, A., Phillips, D., Fischer, W., et al. (2019) · Journal of Computational Chemistry

Parallel Computing and Optimization TechniquesProtein Structure and Dynamics

Cited by 114

A Survey on Uncertainty Estimation in Deep Learning Classification Systems from a Bayesian Perspective

Mena, J., Pujol, O., & Vitrià, J. (2022) · ACM Computing Surveys

Adversarial Robustness in Machine LearningAdvanced Neural Network Applications

Cited by 114

A systematic review on federated learning in medical image analysis

Sohan, M. F., & Basalamah, A. (2023) · IEEE Access

Privacy-Preserving Technologies in DataArtificial Intelligence in Healthcare and Education

Cited by 114

Decentralized Stochastic Optimization and Machine Learning: A Unified Variance-Reduction Framework for Robust Performance and Fast Convergence

Xin, R., Kar, S., & Khan, U. A. (2020) · IEEE Signal Processing Magazine

Stochastic Gradient Optimization TechniquesDistributed Control Multi-Agent Systems

Cited by 114

Recurrent Scene Parsing with Perspective Understanding in the Loop

Kong, S., & Fowlkes, C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Advanced Neural Network ApplicationsAdvanced Vision and Imaging

Cited by 114

Quantum Annealing for Constrained Optimization

Hen, I., & Spedalieri, F. M. (2016) · Physical Review Applied

Quantum Computing Algorithms and ArchitectureMetaheuristic Optimization Algorithms Research

Cited by 114

Improving code readability models with textual features

Scalabrino, S., Linares-Vasquez, M., Poshyvanyk, D., & Oliveto, R. (2016) · 2016 IEEE 24th International Conference on Program Comprehension (ICPC)

Software Engineering ResearchText Readability and Simplification

Cited by 114

Probabilistic Recovery of Incomplete Sensed Data in IoT

Fekade, B., Maksymyuk, T., Kyryk, M., & Jo, M. (2018) · IEEE Internet of Things Journal

IoT and Edge/Fog ComputingIoT Networks and Protocols

Cited by 114

Masm: a multiple-algorithm service model for energy-delay optimization in edge artificial intelligence

Zhang, W., Zhang, Z., Zeadally, S., Chao, H.-C., & Leung, V. C. M. (2019) · IEEE Transactions on Industrial Informatics

IoT and Edge/Fog ComputingEnergy Efficiency in Computing

Cited by 114

Dynamic Cloth Manipulation with Deep Reinforcement Learning

Jangir, R., Alenya, G., & Torras, C. (2020) · 2020 IEEE International Conference on Robotics and Automation (ICRA)

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 114

Large Language Models for Software Engineering: A Systematic Literature Review

Hou, X., Zhao, Y., Liu, Y., Yang, Z., Wang, K., Li, L., Luo, X., Lo, D., Grundy, J., & Wang, H. (2023) · arXiv

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 114

AI and accessibility

Morris, M. R. (2020) · Communications of the ACM

Ethics and Social Impacts of AIDigital Accessibility for Disabilities

Cited by 114

Assessing Generalization in Deep Reinforcement Learning

Packer, C., Gao, K., Kos, J., Krähenbühl, P., Koltun, V., & Song, D. (2018) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 114

Next generation technologies for smart healthcare: challenges, vision, model, trends and future directions

Tuli, S., Tuli, S., Wander, G., Wander, P., Gill, S. S., Dustdar, S., Sakellariou, R., & Rana, O. (2020) · Internet Technology Letters

Internet of Things and AIIoT and Edge/Fog Computing

Cited by 114

Whose Opinions Do Language Models Reflect?

Santurkar, S., Durmus, E., Ladhak, F., Lee, C., Liang, P., & Hashimoto, T. (2023) · arXiv

Sentiment Analysis and Opinion MiningEthics and Social Impacts of AI

Cited by 114

Self Paced Deep Learning for Weakly Supervised Object Detection

Sangineto, E., Nabi, M., Culibrk, D., & Sebe, N. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 114

Extracting Success from IBM’s 20-Qubit Machines Using Error-Aware Compilation

Nishio, S., Pan, Y., Satoh, T., Amano, H., & Meter, R. V. (2020) · ACM Journal on Emerging Technologies in Computing Systems

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 114

Examining the Impact of Self-Admitted Technical Debt on Software Quality

Wehaibi, S., Shihab, E., & Guerrouj, L. (2016) · 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER)

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 114

Backdoor Attacks and Defenses in Federated Learning: State-of-the-Art, Taxonomy, and Future Directions

Gong, X., Chen, Y., Wang, Q., & Kong, W. (2023) · IEEE Wireless Communications

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 114

PyQUBO: Python Library for Mapping Combinatorial Optimization Problems to QUBO Form

Zaman, M., Tanahashi, K., & Tanaka, S. (2022) · IEEE Transactions on Computers

Quantum Computing Algorithms and ArchitectureMetaheuristic Optimization Algorithms Research

Cited by 114

Reviewing Federated Machine Learning and Its Use in Diseases Prediction

Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Sensors

Privacy-Preserving Technologies in DataInternet of Things and AI

Cited by 114

How does the smartphone usage of college students affect academic performance?

Han, S., & Yi, Y. J. (2019) · Journal of Computer Assisted Learning

Mobile Learning in EducationImpact of Technology on Adolescents

Cited by 114

A Systematic Review of Productivity Factors in Software Development

Wagner, S., & Ruhe, M. (2018) · arXiv

Software Engineering Techniques and PracticesSoftware Engineering Research

Cited by 114