Check computer science papers: citations, claims and retractions

Learning Expressionlets on Spatio-temporal Manifold for Dynamic Facial Expression Recognition

Liu, M., Shan, S., Wang, R., & Chen, X. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition

Face and Expression RecognitionEmotion and Mood Recognition

Cited by 392

Deep Bayesian Active Learning with Image Data

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

Machine Learning and AlgorithmsAdvanced Bandit Algorithms Research

Cited by 392

The Intuitive Appeal of Explainable Machines

Selbst, A. D., & Barocas, S. (2018) · SSRN Electronic Journal

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 392

Accurate Decentralized Application Identification via Encrypted Traffic Analysis Using Graph Neural Networks

Shen, M., Zhang, J., Zhu, L., Xu, K., & Du, X. (2021) · IEEE Transactions on Information Forensics and Security

Internet Traffic Analysis and Secure E-votingPrivacy-Preserving Technologies in Data

Cited by 392

Bayesian approach for neural networks—review and case studies

Lampinen, J., & Vehtari, A. (2001) · Neural Networks

Gaussian Processes and Bayesian InferenceNeural Networks and Applications

Cited by 392

Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces

Merrill, M. A., Shaw, A. G., Carlini, N., Li, B., Raj, H., Bercovich, I., Shi, L., Shin, J. Y., Walshe, T., Buchanan, E. K., et al. (2026) · arXiv

AI-based Problem Solving and PlanningArtificial Intelligence Applications

Cited by 392

FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring

Wu, Q., Chen, X., Zhou, Z., & Zhang, J. (2022) · IEEE Transactions on Mobile Computing

Privacy-Preserving Technologies in DataIoT and Edge/Fog Computing

Cited by 392

The Principles and Limits of Algorithm-in-the-Loop Decision Making

Green, B., & Chen, Y. (2019) · Proceedings of the ACM on Human-Computer Interaction

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 392

A Standard Model of the Mind: Toward a Common Computational Framework Across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics

Laird, J. E., Lebiere, C., & Rosenbloom, P. S. (2017) · AI Magazine

Psychiatry, Mental Health, NeuroscienceAI-based Problem Solving and Planning

Cited by 392

A scalable approach to thread-level speculation

Steffan, J. G., Colohan, C. B., Zhai, A., & Mowry, T. C. (2000) · Proceedings of the 27th annual international symposium on Computer architecture - ISCA '00

Parallel Computing and Optimization TechniquesDistributed systems and fault tolerance

Cited by 392

Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider

Shahrad, M., Fonseca, R., Goiri, Í., Chaudhry, G., Batum, P., Cooke, J., Laureano, E., Tresness, C., Russinovich, M., & Bianchini, R. (2020) · arXiv

Cloud Computing and Resource ManagementEnergy Efficiency in Computing

Cited by 391

Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study

Yamashita, R., Long, J., Longacre, T., Peng, L., Berry, G., Martin, B., Higgins, J., Rubin, D. L., & Shen, J. (2021) · The Lancet Oncology

AI in cancer detectionGenetic factors in colorectal cancer

Cited by 391

MSP-IMPROV: An Acted Corpus of Dyadic Interactions to Study Emotion Perception

Busso, C., Parthasarathy, S., Burmania, A., AbdelWahab, M., Sadoughi, N., & Provost, E. M. (2017) · IEEE Transactions on Affective Computing

Emotion and Mood RecognitionSpeech and Audio Processing

Cited by 391

Combining modality specific deep neural networks for emotion recognition in video

Kahou, S. E., Pal, C., Bouthillier, X., Froumenty, P., Gülçehre, Ç., Memisevic, R., Vincent, P., Courville, A., Bengio, Y., Ferrari, R. C., et al. (2013) · Proceedings of the 15th ACM on International conference on multimodal interaction

Emotion and Mood RecognitionFace recognition and analysis

Cited by 391

A survey of Knowledge Discovery and Data Mining process models

KURGAN, L. A., & MUSILEK, P. (2006) · The Knowledge Engineering Review

Data Mining Algorithms and ApplicationsData Mining and Machine Learning Applications

Cited by 391

Robust Network Traffic Classification

Zhang, J., Chen, X., Xiang, Y., Zhou, W., & Wu, J. (2015) · IEEE/ACM Transactions on Networking

Internet Traffic Analysis and Secure E-votingNetwork Security and Intrusion Detection

Cited by 391

UX Design Innovation: Challenges for Working with Machine Learning as a Design Material

Dove, G., Halskov, K., Forlizzi, J., & Zimmerman, J. (2017) · Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

Innovative Human-Technology InteractionUsability and User Interface Design

Cited by 391

A Race-Track Trapped-Ion Quantum Processor

Moses, S. A., Baldwin, C. H., Allman, M. S., Ancona, R., Ascarrunz, L., Barnes, C., Bartolotta, J., Bjork, B., Blanchard, P., Bohn, M., et al. (2023) · Physical Review X

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 391

Biases in electronic health record data due to processes within the healthcare system: retrospective observational study

Agniel, D., Kohane, I. S., & Weber, G. M. (2018) · BMJ

Machine Learning in HealthcareElectronic Health Records Systems

Cited by 391

Generality in artificial intelligence

McCarthy, J. (1987) · Communications of the ACM

AI-based Problem Solving and PlanningLogic, Reasoning, and Knowledge

Cited by 391

Applying Systematic Reviews to Diverse Study Types: An Experience Report

Dyba, T., Dingsoyr, T., & Hanssen, G. K. (2007) · First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007)

Software Engineering Techniques and PracticesMeta-analysis and systematic reviews

Cited by 391

Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent

Chen, Y., Su, L., & Xu, J. (2017) · Proceedings of the ACM on Measurement and Analysis of Computing Systems

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 390

Leveraging Frequency Analysis for Deep Fake Image Recognition

Frank, J., Eisenhofer, T., Schönherr, L., Fischer, A., Kolossa, D., & Holz, T. (2020) · arXiv

Digital Media Forensic DetectionGenerative Adversarial Networks and Image Synthesis

Cited by 390

RWKV: Reinventing RNNs for the Transformer Era

Peng, B., Alcaide, E., Anthony, Q., Albalak, A., Arcadinho, S., Biderman, S., Cao, H., Cheng, X., Chung, M., Derczynski, L., et al. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023

Topic ModelingNatural Language Processing Techniques

Cited by 390