Check computer science papers: citations, claims and retractions

GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models

Chen, D., Yu, N., Zhang, Y., & Fritz, M. (2020) · Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 311

A primer on learning in Bayesian networks for computational biology

Needham, C. J., Bradford, J. R., Bulpitt, A. J., & Westhead, D. R. (2007) · PLoS Computational Biology

Bayesian Modeling and Causal InferenceGene Regulatory Network Analysis

Cited by 311

"You Might Also Like:" Privacy Risks of Collaborative Filtering

Calandrino, J. A., Kilzer, A., Narayanan, A., Felten, E. W., & Shmatikov, V. (2011) · 2011 IEEE Symposium on Security and Privacy

Privacy-Preserving Technologies in DataPrivacy, Security, and Data Protection

Cited by 311

"Algorithms ruin everything": #RIPTwitter, Folk Theories, and Resistance to Algorithmic Change in Social Media

DeVito, M. A., Gergle, D., & Birnholtz, J. (2017) · Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

Ethics and Social Impacts of AIMedia and Digital Communication

Cited by 311

General Type-2 Fuzzy Logic Systems Made Simple: A Tutorial

Mendel, J. M. (2014) · IEEE Transactions on Fuzzy Systems

Fuzzy Logic and Control SystemsAdvanced Algebra and Logic

Cited by 311

The promises and perils of mining git

Bird, C., Rigby, P. C., Barr, E. T., Hamilton, D. J., German, D. M., & Devanbu, P. (2009) · 2009 6th IEEE International Working Conference on Mining Software Repositories

Software Engineering ResearchOpen Source Software Innovations

Cited by 311

Solving olympiad geometry without human demonstrations

Trinh, T. H., Wu, Y., Le, Q. V., He, H., & Luong, T. (2024) · Nature

AI-based Problem Solving and PlanningArtificial Intelligence Applications

Cited by 311

Pre-training of Graph Augmented Transformers for Medication Recommendation

Shang, J., Ma, T., Xiao, C., & Sun, J. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

Machine Learning in HealthcareRecommender Systems and Techniques

Cited by 311

Efficient multi-objective neural architecture search via Lamarckian evolution

Elsken, T., Metzen, J. H., & Hutter, F. (2018) · arXiv

Evolutionary Algorithms and ApplicationsAdvanced Neural Network Applications

Cited by 311

Benchmarking Retrieval-Augmented Generation for Medicine

Xiong, G., Jin, Q., Lu, Z., & Zhang, A. (2024) · Findings of the Association for Computational Linguistics ACL 2024

Topic ModelingInformation Retrieval and Search Behavior

Cited by 310

Open Quantum Assembly Language

Cross, A. W., Bishop, L. S., Smolin, J. A., & Gambetta, J. M. (2017) · arXiv

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 310

Comparing oversampling techniques to handle the class imbalance problem: a customer churn prediction case study

Amin, A., Anwar, S., Adnan, A., Nawaz, M., Howard, N., Qadir, J., Hawalah, A., & Hussain, A. (2016) · IEEE Access

Imbalanced Data Classification TechniquesCustomer churn and segmentation

Cited by 310

Artificial intelligence ethics guidelines for developers and users: clarifying their content and normative implications

Ryan, M., & Stahl, B. C. (2021) · Journal of Information, Communication and Ethics in Society

Ethics and Social Impacts of AIEthics in Business and Education

Cited by 310

Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning

Horng, S., Sontag, D. A., Halpern, Y., Jernite, Y., Shapiro, N. I., & Nathanson, L. A. (2017) · PLOS ONE

Machine Learning in HealthcareSepsis Diagnosis and Treatment

Cited by 310

On the Robustness of Interpretability Methods

Alvarez-Melis, D., & Jaakkola, T. S. (2018) · arXiv

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 310

ChatGPT: Fundamentals, Applications and Social Impacts

Abdullah, M., Madain, A., & Jararweh, Y. (2022) · 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)

Topic ModelingNatural Language Processing Techniques

Cited by 310

BodyScope: a wearable acoustic sensor for activity recognition

Yatani, K., & Truong, K. N. (2012) · Proceedings of the 2012 ACM Conference on Ubiquitous Computing

Context-Aware Activity Recognition SystemsPhonocardiography and Auscultation Techniques

Cited by 310

High-Performance Long-Term Tracking With Meta-Updater

Dai, K., Zhang, Y., Wang, D., Li, J., Lu, H., & Yang, X. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Video Surveillance and Tracking MethodsVisual Attention and Saliency Detection

Cited by 310

The ai-based cyber threat landscape: A survey

Kaloudi, N., & Li, J. (2021) · ACM Computing Surveys

Adversarial Robustness in Machine LearningNetwork Security and Intrusion Detection

Cited by 310

Psychophysiological responses as indices of affective dimensions

VanOYEN WITVLIET, C., & VRANA, S. R. (1995) · Psychophysiology

Emotion and Mood RecognitionHeart Rate Variability and Autonomic Control

Cited by 310

An Overview of the World of MOOCs

Baturay, M. H. (2015) · Procedia - Social and Behavioral Sciences

Online Learning and AnalyticsOpen Education and E-Learning

Cited by 310

Large-Scale Study of Perceptual Video Quality

Sinno, Z., & Bovik, A. C. (2019) · IEEE Transactions on Image Processing

Image and Video Quality AssessmentImage Retrieval and Classification Techniques

Cited by 310

Data Poisoning Attacks on Federated Machine Learning

Sun, G., Cong, Y., Dong, J., Wang, Q., Lyu, L., & Liu, J. (2022) · IEEE Internet of Things Journal

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 310

Cloud computing for genomic data analysis and collaboration

Langmead, B., & Nellore, A. (2018) · Nature Reviews Genetics

Cloud Computing and Resource ManagementScientific Computing and Data Management

Cited by 310