Check computer science papers: citations, claims and retractions

Scalable Private Learning with PATE

Papernot, N., Song, S., Mironov, I., Raghunathan, A., Talwar, K., & Erlingsson, Ú. (2018) · arXiv

Privacy-Preserving Technologies in DataCryptography and Data Security

Cited by 373

C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models

Huang, Y., Bai, Y., Zhu, Z., Zhang, J., Zhang, J., Su, T., Liu, J., Lv, C., Zhang, Y., Lei, J., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 373

Deep Fluids: A Generative Network for Parameterized Fluid Simulations

Kim, B., Azevedo, V. C., Thuerey, N., Kim, T., Gross, M., & Solenthaler, B. (2019) · Computer Graphics Forum

Computer Graphics and Visualization Techniques3D Shape Modeling and Analysis

Cited by 372

Hamiltonian Simulation with Nearly Optimal Dependence on all Parameters

Berry, D. W., Childs, A. M., & Kothari, R. (2015) · 2015 IEEE 56th Annual Symposium on Foundations of Computer Science

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 372

Overhead and noise threshold of fault-tolerant quantum error correction

Steane, A. M. (2003) · Physical Review A

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 372

Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Cohen, N., & Hoshen, Y. (2020) · arXiv

Anomaly Detection Techniques and ApplicationsAdvanced Neural Network Applications

Cited by 372

Fast support vector machine training and classification on graphics processors

Catanzaro, B., Sundaram, N., & Keutzer, K. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08

Parallel Computing and Optimization TechniquesStochastic Gradient Optimization Techniques

Cited by 372

Bias in AI-based models for medical applications: challenges and mitigation strategies

Mittermaier, M., Raza, M. M., & Kvedar, J. C. (2023) · npj Digital Medicine

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 372

Is clinical virtual reality ready for primetime?

Rizzo, A. “., & Koenig, S. T. (2017) · Neuropsychology

Virtual Reality Applications and ImpactsSimulation-Based Education in Healthcare

Cited by 372

A DCT Statistics-Based Blind Image Quality Index

Saad, M. A., Bovik, A. C., & Charrier, C. (2010) · IEEE Signal Processing Letters

Image and Video Quality AssessmentImage Retrieval and Classification Techniques

Cited by 372

Blockchain for IoT-based healthcare: background, consensus, platforms, and use cases

Ray, P. P., Dash, D., Salah, K., & Kumar, N. (2021) · IEEE Systems Journal

Blockchain Technology Applications and SecurityInternet of Things and AI

Cited by 372

Recommendations for Methodology of Virtual Reality Clinical Trials in Health Care by an International Working Group: Iterative Study

Birckhead, B., Khalil, C., Liu, X., Conovitz, S., Rizzo, A., Danovitch, I., Bullock, K., & Spiegel, B. (2019) · JMIR Mental Health

Virtual Reality Applications and ImpactsDigital Mental Health Interventions

Cited by 372

Explainable, trustworthy, and ethical machine learning for healthcare: A survey

Rasheed, K., Qayyum, A., Ghaly, M., Al-Fuqaha, A., Razi, A., & Qadir, J. (2022) · Computers in Biology and Medicine

Explainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare and Education

Cited by 372

Self-supervised speech representation learning: A review

Mohamed, A., Lee, H.-Y., Borgholt, L., Havtorn, J. D., Edin, J., Igel, C., Kirchhoff, K., Li, S.-W., Livescu, K., Maaløe, L., et al. (2022) · IEEE Journal of Selected Topics in Signal Processing

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 372

“StarGAN-VC: non-parallel many-to-many voice conversion using star generative adversarial networks,”

Kameoka, H., Kaneko, T., Tanaka, K., & Hojo, N. (2018) · 2018 IEEE Spoken Language Technology Workshop (SLT)

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 371

Affect-aware tutors: recognising and responding to student affect

Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., & Picard, R. (2009) · International Journal of Learning Technology

Intelligent Tutoring Systems and Adaptive LearningEmotion and Mood Recognition

Cited by 371

The impact of generative artificial intelligence on socioeconomic inequalities and policy making

Capraro, V., Lentsch, A., Acemoglu, D., Akgun, S., Akhmedova, A., Bilancini, E., Bonnefon, J.-F., Brañas-Garza, P., Butera, L., Douglas, K. M., et al. (2024) · PNAS Nexus

Ethics and Social Impacts of AITechnology's Impact on Media

Cited by 371

ShieldFL: Mitigating Model Poisoning Attacks in Privacy-Preserving Federated Learning

Ma, Z., Ma, J., Miao, Y., Li, Y., & Deng, R. H. (2022) · IEEE Transactions on Information Forensics and Security

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 371

Chaos-based random number generators-part I: analysis [cryptography]

Stojanovski, T., & Kocarev, L. (2001) · IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications

Chaos-based Image/Signal EncryptionMathematical Dynamics and Fractals

Cited by 371

Computer-assisted imaging to assess brain structure in healthy and diseased brains

Ashburner, J., Csernansk, J. G., Davatzikos, C., Fox, N. C., Frisoni, G. B., & Thompson, P. M. (2003) · The Lancet Neurology

Medical Image Segmentation TechniquesDementia and Cognitive Impairment Research

Cited by 371

Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Yu, J., Xu, Y., Koh, J. Y., Luong, T., Baid, G., Wang, Z., Vasudevan, V., Ku, A., Yang, Y., Ayan, B. K., et al. (2022) · arXiv

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 371

Shorthand writing on stylus keyboard

Zhai, S., & Kristensson, P.-O. (2003) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Interactive and Immersive DisplaysUsability and User Interface Design

Cited by 371

Personalized Messages That Promote Science Learning in Virtual Environments.

Moreno, R., & Mayer, R. E. (2004) · Journal of Educational Psychology

Virtual Reality Applications and ImpactsIntelligent Tutoring Systems and Adaptive Learning

Cited by 370

Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning

Xie, Z., Lin, Y., Zhang, Z., Cao, Y., Lin, S., & Hu, H. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 370