Check computer science papers: citations, claims and retractions

What Makes Good In-Context Examples for GPT-$3$?

Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., & Chen, W. (2021) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 148

The risks of using ChatGPT to obtain common safety-related information and advice

Oviedo-Trespalacios, O., Peden, A. E., Cole-Hunter, T., Costantini, A., Haghani, M., Rod, J. E., Kelly, S., Torkamaan, H., Tariq, A., David Albert Newton, J., et al. (2023) · Safety Science

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 148

Machine Learning that Matters

Wagstaff, K. (2012) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 148

Cachier: Edge-Caching for Recognition Applications

Drolia, U., Guo, K., Tan, J., Gandhi, R., & Narasimhan, P. (2017) · 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)

IoT and Edge/Fog ComputingCaching and Content Delivery

Cited by 148

Focus beyond Quadratic Speedups for Error-Corrected Quantum Advantage

Babbush, R., McClean, J. R., Newman, M., Gidney, C., Boixo, S., & Neven, H. (2021) · PRX Quantum

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 148

Sparse Blossom: correcting a million errors per core second with minimum-weight matching

Higgott, O., & Gidney, C. (2023) · arXiv

Quantum Computing Algorithms and ArchitectureError Correcting Code Techniques

Cited by 148

The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence

Watson, D. (2019) · Minds and Machines

Ethics and Social Impacts of AICybernetics and Technology in Society

Cited by 148

Robust 3D Shape Correspondence in the Spectral Domain

Jain, V., & Zhang, H. (2006) · IEEE International Conference on Shape Modeling and Applications 2006 (SMI'06)

3D Shape Modeling and AnalysisComputer Graphics and Visualization Techniques

Cited by 148

Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems

Liu, B., Tür, G., Hakkani-Tür, D., Shah, P., & Heck, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Speech and dialogue systemsIntelligent Tutoring Systems and Adaptive Learning

Cited by 148

Generative AI in cybersecurity: A comprehensive review of LLM applications and vulnerabilities

Ferrag, M. A., Alwahedi, F., Battah, A., Cherif, B., Mechri, A., Tihanyi, N., Bisztray, T., & Debbah, M. (2025) · Internet of Things and Cyber-Physical Systems

Adversarial Robustness in Machine LearningNetwork Security and Intrusion Detection

Cited by 148

Why machine learning algorithms fail in misuse detection on KDD intrusion detection data set

Sabhnani, M., & Serpen, G. (2004) · Intelligent Data Analysis

Network Security and Intrusion DetectionArtificial Immune Systems Applications

Cited by 148

Putting the Object Back into Video Object Segmentation

Cheng, H. K., Oh, S. W., Price, B., Lee, J.-Y., & Schwing, A. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 148

To explain or not to explain: the effects of personal characteristics when explaining music recommendations

Millecamp, M., Htun, N. N., Conati, C., & Verbert, K. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces

Recommender Systems and TechniquesExplainable Artificial Intelligence (XAI)

Cited by 148

Learning to Detect Fake Face Images in the Wild

Hsu, C.-C., Lee, C.-Y., & Zhuang, Y.-X. (2018) · 2018 International Symposium on Computer, Consumer and Control (IS3C)

Digital Media Forensic DetectionGenerative Adversarial Networks and Image Synthesis

Cited by 148

Parallel training of DNNs with Natural Gradient and Parameter Averaging

Povey, D., Zhang, X., & Khudanpur, S. (2014) · arXiv

Speech Recognition and SynthesisStochastic Gradient Optimization Techniques

Cited by 148

Known or knowing publics? Social media data mining and the question of public agency

Kennedy, H., & Moss, G. (2015) · Big Data & Society

Ethics and Social Impacts of AIBig Data Technologies and Applications

Cited by 148

Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval

Wu, G., Han, J., Guo, Y., Liu, L., Ding, G., Ni, Q., & Shao, L. (2019) · IEEE Transactions on Image Processing

Advanced Image and Video Retrieval TechniquesImage Retrieval and Classification Techniques

Cited by 148

What can I say?: addressing user experience challenges of a mobile voice user interface for accessibility

Corbett, E., & Weber, A. (2016) · Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services

Usability and User Interface DesignDigital Accessibility for Disabilities

Cited by 148

Bias and ethics of AI systems applied in auditing - A systematic review

Murikah, W., Nthenge, J. K., & Musyoka, F. M. (2024) · Scientific African

Ethics and Social Impacts of AIRobotic Process Automation Applications

Cited by 148

Neurological and robot-controlled induction of an apparition

Blanke, O., Pozeg, P., Hara, M., Heydrich, L., Serino, A., Yamamoto, A., Higuchi, T., Salomon, R., Seeck, M., Landis, T., et al. (2014) · Current Biology

Virtual Reality Applications and ImpactsFree Will and Agency

Cited by 148

Improving Multiple Pedestrian Tracking by Track Management and Occlusion Handling

Stadler, D., & Beyerer, J. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Video Surveillance and Tracking MethodsAutonomous Vehicle Technology and Safety

Cited by 147

Guidelines for the Search Strategy to Update Systematic Literature Reviews in Software Engineering

Wohlin, C., Mendes, E., Felizardo, K. R., & Kalinowski, M. (2020) · Information and Software Technology

Software Engineering Techniques and PracticesMeta-analysis and systematic reviews

Cited by 147

Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images

Yu, G., Sun, K., Xu, C., Shi, X.-H., Wu, C., Xie, T., Meng, R.-Q., Meng, X.-H., Wang, K.-S., Xiao, H.-M., et al. (2021) · Nature Communications

AI in cancer detectionColorectal Cancer Screening and Detection

Cited by 147

Student Vulnerability, Agency and Learning Analytics: An Exploration

Prinsloo, P., & Slade, S. (2016) · Journal of Learning Analytics

Online Learning and AnalyticsData Privacy and Cybersecurity

Cited by 147