Check computer science papers: citations, claims and retractions

Deep Bayesian Active Learning with Image Data

Gal, Y., Islam, R., & Ghahramani, Z. (2017) · arXiv

Machine Learning and AlgorithmsAdvanced Bandit Algorithms Research

Cited by 900

Quantum computers and dissipation

Palma, G. M., Suominen, K., & EKERT, A. K. (1996) · Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 899

Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets

Peng, Y., Yan, S., & Lu, Z. (2019) · Proceedings of the 18th BioNLP Workshop and Shared Task

Topic ModelingMachine Learning in Healthcare

Cited by 899

Robust real-time face detection

Viola, P., & Jones, M. (2005) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001

Face recognition and analysisFace and Expression Recognition

Cited by 898

An Efficient Algorithm for Exploiting Multiple Arithmetic Units

Tomasulo, R. M. (1967) · IBM Journal of Research and Development

Parallel Computing and Optimization TechniquesNumerical Methods and Algorithms

Cited by 898

What can we learn privately?

Kasiviswanathan, S. P., Lee, H. K., Nissim, K., Raskhodnikova, S., & Smith, A. (2011) · SIAM Journal on Computing

Privacy-Preserving Technologies in DataCryptography and Data Security

Cited by 898

Managing artificial intelligence

Berente, N., Gu, B., Recker, J., & Santhanam, R. (2021) · MIS Quarterly

Artificial Intelligence ApplicationsInformation Technology Governance and Strategy

Cited by 898

A Persona-Based Neural Conversation Model

Li, J., Galley, M., Brockett, C., Spithourakis, G., Gao, J., & Dolan, B. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Speech and dialogue systemsAI in Service Interactions

Cited by 898

Machine Learning Testing: Survey, Landscapes and Horizons

Zhang, J. M., Harman, M., Ma, L., & Liu, Y. (2022) · IEEE Transactions on Software Engineering

Software Testing and Debugging TechniquesAdversarial Robustness in Machine Learning

Cited by 897

A Lip Sync Expert Is All You Need for Speech to Lip Generation In the Wild

Prajwal, K. R., Mukhopadhyay, R., Namboodiri, V. P., & Jawahar, C. V. (2020) · Proceedings of the 28th ACM International Conference on Multimedia

Speech and Audio ProcessingGenerative Adversarial Networks and Image Synthesis

Cited by 897

Self-report captures 27 distinct categories of emotion bridged by continuous gradients

Cowen, A. S., & Keltner, D. (2017) · Proceedings of the National Academy of Sciences

Emotion and Mood RecognitionEmotions and Moral Behavior

Cited by 897

A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME

Salih, A. M., Raisi‐Estabragh, Z., Galazzo, I. B., Radeva, P., Petersen, S. E., Lekadir, K., & Menegaz, G. (2025) · Advanced Intelligent Systems

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 897

Artificial Intelligence, Values, and Alignment

Gabriel, I. (2020) · Minds and Machines

Ethics and Social Impacts of AIPhilosophical Ethics and Theory

Cited by 896

Speaker Recognition from Raw Waveform with SincNet

Ravanelli, M., & Bengio, Y. (2018) · 2018 IEEE Spoken Language Technology Workshop (SLT)

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 896

Power of data in quantum machine learning

Huang, H.-Y., Broughton, M., Mohseni, M., Babbush, R., Boixo, S., Neven, H., & McClean, J. R. (2021) · Nature Communications

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 896

Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners

Bisong, E. (2019) · Apress eBooks

Cloud Computing and Resource ManagementArtificial Intelligence Applications

Cited by 895

Transfer Learning for Visual Categorization: A Survey

Shao, L., Zhu, F., & Li, X. (2015) · IEEE Transactions on Neural Networks and Learning Systems

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 895

Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making

Zhang, Y., Liao, Q. V., & Bellamy, R. K. E. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency

Explainable Artificial Intelligence (XAI)Human-Automation Interaction and Safety

Cited by 895

Motivation, Governance, and the Viability of Hybrid Forms in Open Source Software Development

Shah, S. K. (2006) · Management Science

Open Source Software InnovationsSoftware Engineering Techniques and Practices

Cited by 894

The sequence read archive: explosive growth of sequencing data

Kodama, Y., Shumway, M., Leinonen, R., & on behalf of the International Nucleotide Sequence Database Collaboration (2012) · Nucleic Acids Research

Research Data Management PracticesGenomics and Phylogenetic Studies

Cited by 894

K-BERT: Enabling Language Representation with Knowledge Graph

Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., & Wang, P. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence

Topic ModelingAdvanced Graph Neural Networks

Cited by 894

LEAF: A Benchmark for Federated Settings

Caldas, S., Duddu, S. M. K., Wu, P., Li, T., Konečný, J., McMahan, H. B., Smith, V., & Talwalkar, A. (2018) · arXiv

Privacy-Preserving Technologies in DataDomain Adaptation and Few-Shot Learning

Cited by 894

A Process Model of the Formation of Spatial Presence Experiences

Wirth, W., Hartmann, T., Böcking, S., Vorderer, P., Klimmt, C., Schramm, H., Saari, T., Laarni, J., Ravaja, N., Gouveia, F. R., et al. (2007) · Media Psychology

Virtual Reality Applications and ImpactsMedia Influence and Health

Cited by 893

The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation

Chicco, D., Tötsch, N., & Jurman, G. (2021) · BioData Mining

Imbalanced Data Classification TechniquesMachine Learning and Data Classification

Cited by 893