Check computer science papers: citations, claims and retractions

Ethical governance is essential to building trust in robotics and artificial intelligence systems

Winfield, A. F. T., & Jirotka, M. (2018) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences

Ethics and Social Impacts of AIDigitalization, Law, and Regulation

Cited by 493

LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory

Wu, D., Wang, H., Yu, W., Zhang, Y., Chang, K.-W., & Yu, D. (2024) · arXiv

Speech and dialogue systemsInformation Retrieval and Search Behavior

Cited by 493

Psychologically induced cooling of a specific body part caused by the illusory ownership of an artificial counterpart

Moseley, G. L., Olthof, N., Venema, A., Don, S., Wijers, M., Gallace, A., & Spence, C. (2008) · Proceedings of the National Academy of Sciences

Virtual Reality Applications and ImpactsTactile and Sensory Interactions

Cited by 493

The Metacognitive Demands and Opportunities of Generative AI

Tankelevitch, L., Kewenig, V., Simkute, A., Scott, A. E., Sarkar, A., Sellen, A., & Rintel, S. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems

Usability and User Interface DesignHuman-Automation Interaction and Safety

Cited by 493

Efficient Energy Management for the Internet of Things in Smart Cities

Ejaz, W., Naeem, M., Shahid, A., Anpalagan, A., & Jo, M. (2017) · IEEE Communications Magazine

IoT and Edge/Fog ComputingEnergy Harvesting in Wireless Networks

Cited by 492

Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

Papernot, N., Abadi, M., Erlingsson, Ú., Goodfellow, I., & Talwar, K. (2016) · arXiv

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 492

Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing

Dolui, K., & Datta, S. K. (2017) · 2017 Global Internet of Things Summit (GIoTS)

IoT and Edge/Fog ComputingCloud Computing and Resource Management

Cited by 492

HAWQ: Hessian AWare Quantization of Neural Networks With Mixed-Precision

Dong, Z., Yao, Z., Gholami, A., Mahoney, M., & Keutzer, K. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 492

YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO

Hussain, M. (2024) · IEEE Access

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 492

Mitigating Gender Bias in Natural Language Processing: Literature Review

Sun, T., Gaut, A., Tang, S., Huang, Y., ElSherief, M., Zhao, J., Mirza, D., Belding, E., Chang, K.-W., & Wang, W. Y. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Ethics and Social Impacts of AIGender Studies in Language

Cited by 492

Virtual reality exposure therapy of anxiety disorders: A review

Krijn, M., Emmelkamp, P. M. G., Olafsson, R. P., & Biemond, R. (2004) · Clinical Psychology Review

Virtual Reality Applications and ImpactsAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes

Cited by 492

The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Gao, L., Biderman, S., Black, S., Golding, L., Hoppe, T., Foster, C., Phang, J., He, H., Thite, A., Nabeshima, N., et al. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 492

Training deep neural networks on imbalanced data sets

Wang, S., Liu, W., Wu, J., Cao, L., Meng, Q., & Kennedy, P. J. (2016) · 2016 International Joint Conference on Neural Networks (IJCNN)

Imbalanced Data Classification TechniquesMachine Learning and Data Classification

Cited by 491

Role play with large language models

Shanahan, M., McDonell, K., & Reynolds, L. (2023) · Nature

Speech and dialogue systemsSocial Robot Interaction and HRI

Cited by 491

Variational Continual Learning

Nguyen, C. V., Li, Y., Bui, T. D., & Turner, R. E. (2017) · arXiv

Domain Adaptation and Few-Shot LearningData Stream Mining Techniques

Cited by 491

Overfitting and neural networks: conjugate gradient and backpropagation

Lawrence, S., & Giles, C. L. (2000) · Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium

Neural Networks and ApplicationsStochastic Gradient Optimization Techniques

Cited by 491

Meta-analysis of the impact of Augmented Reality on students’ learning gains

Garzón, J., & Acevedo, J. (2019) · Educational Research Review

Augmented Reality ApplicationsVirtual Reality Applications and Impacts

Cited by 491

On the power of quantum computation

Simon, D. R. (2002) · Proceedings 35th Annual Symposium on Foundations of Computer Science

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 491

Role-Play with Large Language Models

Shanahan, M., McDonell, K., & Reynolds, L. (2023) · arXiv

Speech and dialogue systemsSocial Robot Interaction and HRI

Cited by 490

Recent Advances in Adversarial Training for Adversarial Robustness

Bai, T., Luo, J., Zhao, J., Wen, B., & Wang, Q. (2021) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 490

Four Responsibility Gaps with Artificial Intelligence: Why they Matter and How to Address them

Santoni de Sio, F., & Mecacci, G. (2021) · Philosophy & Technology

Ethics and Social Impacts of AIDigitalization, Law, and Regulation

Cited by 490

ChatDev: Communicative Agents for Software Development

Qian, C., Liu, W., Liu, H., Chen, N., Dang, Y., Li, J., Yang, C., Chen, W., Su, Y., Cong, X., et al. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 490

OSKI: A library of automatically tuned sparse matrix kernels

Vuduc, R., Demmel, J. W., & Yelick, K. A. (2005) · Journal of Physics: Conference Series

Parallel Computing and Optimization TechniquesMatrix Theory and Algorithms

Cited by 490

MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance

Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C. M., & Eger, S. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 490