Check computer science papers: citations, claims and retractions

Inclusive data visualization for people with disabilities: a call to action

Marriott, K., Lee, B., Butler, M., Cutrell, E., Ellis, K., Goncu, C., Hearst, M., McCoy, K., & Szafir, D. A. (2021) · Interactions

Data Visualization and AnalyticsDigital Accessibility for Disabilities

Cited by 83

Fair market value of bitcoin: Halving effect

Meynkhard, A. (2019) · Investment Management and Financial Innovations

Blockchain Technology Applications and SecurityFinancial Markets and Investment Strategies

Cited by 83

Street-Level Algorithms and AI in Bureaucratic Decision-Making: A Caseworker Perspective

Ammitzbøll Flügge, A., Hildebrandt, T., & Møller, N. H. (2021) · Proceedings of the ACM on Human-Computer Interaction

Ethics and Social Impacts of AIE-Government and Public Services

Cited by 83

Data Analytics and Decision- Making in Education: Towards the Educational Data Scientist as a Key Actor in Schools and Higher Education Institutions.

Agasisti, T., & Bowers, A. J. (2017) · Handbook of Contemporary Education Economics

Online Learning and AnalyticsBig Data and Business Intelligence

Cited by 83

Transformers for Vision: A Survey on Innovative Methods for Computer Vision

Palanisamy, B., Hassija, V., Chatterjee, A., Mandal, A., Chakraborty, D., Pandey, A., Chalapathi, G. S. S., & Kumar, D. (2025) · IEEE Access

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 83

A semantic graph-based approach to biomedical summarisation

Plaza, L., Díaz, A., & Gervás, P. (2011) · Artificial Intelligence in Medicine

Advanced Text Analysis TechniquesBiomedical Text Mining and Ontologies

Cited by 83

Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning

Wang, Z., Hunt, J. J., & Zhou, M. (2022) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 83

Phasic Policy Gradient

Cobbe, K., Hilton, J., Klimov, O., & Schulman, J. (2020) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 83

LearningQ: A Large-Scale Dataset for Educational Question Generation

Chen, G., Yang, J., Hauff, C., & Houben, G.-J. (2018) · Proceedings of the International AAAI Conference on Web and Social Media

Topic ModelingNatural Language Processing Techniques

Cited by 83

Deep Learning Artificial Intelligence Predicts Homologous Recombination Deficiency and Platinum Response From Histologic Slides

Bergstrom, E. N., Abbasi, A., Díaz-Gay, M., Galland, L., Ladoire, S., Lippman, S. M., & Alexandrov, L. B. (2024) · Journal of Clinical Oncology

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 83

A Fault-Tolerant Honeycomb Memory

Gidney, C., Newman, M., Fowler, A., & Broughton, M. (2021) · Quantum

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 83

Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection

Chen, S., Zhang, F., Sone, K., & Roth, D. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingAdvanced Text Analysis Techniques

Cited by 83

Graph Neural Network-Based Diagnosis Prediction

Li, Y., Qian, B., Zhang, X., & Liu, H. (2020) · Big Data

Machine Learning in HealthcareAdvanced Graph Neural Networks

Cited by 83

Psychometric Properties of the Persian Language Version of the System Usability Scale

Dianat, I., Ghanbari, Z., & AsghariJafarabadi, M. (2014) · Tabriz University of Medical Sciences

Usability and User Interface DesignHealth Education and Validation

Cited by 83

Compressed Sensing for Multidimensional Spectroscopy Experiments

Sanders, J. N., Saikin, S. K., Mostame, S., Andrade, X., Widom, J. R., Marcus, A. H., & Aspuru-Guzik, A. (2012) · The Journal of Physical Chemistry Letters

Sparse and Compressive Sensing TechniquesMathematical Analysis and Transform Methods

Cited by 83

Language GANs Falling Short

Caccia, M., Caccia, L., Fedus, W., Larochelle, H., Pineau, J., & Charlin, L. (2018) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 83

Detecting the impact of subject characteristics on machine learning-based diagnostic applications

Chaibub Neto, E., Pratap, A., Perumal, T. M., Tummalacherla, M., Snyder, P., Bot, B. M., Trister, A. D., Friend, S. H., Mangravite, L., & Omberg, L. (2019) · npj Digital Medicine

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 83

A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle

Suresh, H., & Guttag, J. (2021) · Equity and Access in Algorithms, Mechanisms, and Optimization

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 82

YolactEdge: Real-time Instance Segmentation on the Edge

Liu, H., Rivera Soto, R. A., Xiao, F., & Jae Lee, Y. (2021) · 2021 IEEE International Conference on Robotics and Automation (ICRA)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 82

MagicVideo: Efficient Video Generation With Latent Diffusion Models

Zhou, D., Wang, W., Yan, H., Lv, W., Zhu, Y., & Feng, J. (2022) · arXiv

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 82

Dynamic Access Control and Authorization System based on Zero-trust architecture

Yao, Q., Wang, Q., Zhang, X., & Fei, J. (2020) · 2020 International Conference on Control, Robotics and Intelligent System

Access Control and TrustUser Authentication and Security Systems

Cited by 82

A very preliminary analysis of DALL-E 2

Marcus, G., Davis, E., & Aaronson, S. (2022) · arXiv

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 82

Symbolic architectures for cognition

Newell, A., Rosenbloom, P. S., & Laird, J. E. (1989)

AI-based Problem Solving and PlanningPsychiatry, Mental Health, Neuroscience

Cited by 82

Gated-Attention Architectures for Task-Oriented Language Grounding

Chaplot, D. S., Sathyendra, K. M., Pasumarthi, R. K., Rajagopal, D., & Salakhutdinov, R. (2017) · arXiv

Multimodal Machine Learning ApplicationsReinforcement Learning in Robotics

Cited by 82