Check computer science papers: citations, claims and retractions

Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling

Xiao, L. (2024) · arXiv

Stochastic Gradient Optimization TechniquesDomain Adaptation and Few-Shot Learning

Cited by 25

Position Paper: Challenges Towards Securing Hardware-assisted Execution Environments

Ning, Z., Zhang, F., Shi, W., & Shi, W. (2017) · Proceedings of the Hardware and Architectural Support for Security and Privacy

Security and Verification in ComputingDistributed systems and fault tolerance

Cited by 25

Toward Predicting 30-Day Readmission Among Oncology Patients: Identifying Timely and Actionable Risk Factors

Hwang, S., Urbanowicz, R., Lynch, S., Vernon, T., Bresz, K., Giraldo, C., Kennedy, E., Leabhart, M., Bleacher, T., Ripchinski, M. R., et al. (2023) · JCO Clinical Cancer Informatics

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 25

A Critical Review of Inductive Logic Programming Techniques for Explainable AI

Zhang, Z., Yilmaz, L., & Liu, B. (2024) · IEEE Transactions on Neural Networks and Learning Systems

Explainable Artificial Intelligence (XAI)Logic, Reasoning, and Knowledge

Cited by 25

Reduced space-time and time costs Ising dislocation codes and arbitrary ancillas

Hastings, M. B., & Geller, A. (2015) · Quantum Information and Computation

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 25

“Privacy-preserving quantum machine learning using differential privacy,”

Senekane, M., Mafu, M., & Taele, B. M. (2017) · 2017 IEEE AFRICON

Privacy-Preserving Technologies in DataQuantum Computing Algorithms and Architecture

Cited by 25

An ISO/IEC 15408-2 Compliant Security Auditing System with Blockchain Technology

Cha, S.-C., & Yeh, K.-H. (2018) · 2018 IEEE Conference on Communications and Network Security (CNS)

Blockchain Technology Applications and SecurityBlockchain Technology in Education and Learning

Cited by 25

KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment

Wang, L., Chen, T., Yuan, W., Zeng, X., Wong, K.-F., & Yin, H. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 25

DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments

Zheng, Y., Fu, D., Hu, X., Cai, X., Ye, L., Lu, P., & Liu, P. (2025) · arXiv

Information Retrieval and Search BehaviorTopic Modeling

Cited by 25

Facilitating Multi-turn Emotional Support Conversation with Positive Emotion Elicitation: A Reinforcement Learning Approach

Zhou, J., Chen, Z., Wang, B., & Huang, M. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Speech and dialogue systemsEmotion and Mood Recognition

Cited by 25

Internet of vehicles: Cloud and fog computing approaches

Desirena Lopez, H. J., Siller, M., & Huerta, I. (2017) · 2017 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)

IoT and Edge/Fog ComputingVehicular Ad Hoc Networks (VANETs)

Cited by 25

Semi-Supervised Music Tagging Transformer

Won, M., Choi, K., & Serra, X. (2021) · arXiv

Music and Audio ProcessingSpeech Recognition and Synthesis

Cited by 25

The four-fifths rule is not disparate impact: a woeful tale of epistemic trespassing in algorithmic fairness

Watkins, E. A., & Chen, J. (2024) · The 2024 ACM Conference on Fairness Accountability and Transparency

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 25

Transformers are Sample Efficient World Models

Micheli, V., Alonso, E., & Fleuret, F. (2022) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 25

Outage Performance of Two-Hop OFDM Systems With Spatially Random Decode-and-Forward Relays

Dang, S., Coon, J. P., & Chen, G. (2017) · IEEE Access

Cooperative Communication and Network CodingAdvanced Wireless Communication Techniques

Cited by 25

Non-cognitive Abilities of Exceptional Software Engineers: A Delphi Study

Groeneveld, W., Jacobs, H., Vennekens, J., & Aerts, K. (2020) · Proceedings of the 51st ACM Technical Symposium on Computer Science Education

Software Engineering Techniques and PracticesHigher Education and Employability

Cited by 25

Leveraging Large Language Models in Conversational Recommender Systems

Friedman, L., Ahuja, S., Allen, D., Tan, Z., Sidahmed, H., Long, C., Xie, J., Schubiner, G., Patel, A., Lara, H., et al. (2023) · arXiv

Recommender Systems and TechniquesAI in Service Interactions

Cited by 25

OES-Fed: a federated learning framework in vehicular network based on noise data filtering

Lei, Y., Wang, S. L., Su, C., & Ng, T. F. (2022) · PeerJ Computer Science

Privacy-Preserving Technologies in DataVehicular Ad Hoc Networks (VANETs)

Cited by 25

Why develop open-source software? The role of non-pecuniary benefits, monetary rewards, and open-source licence type

Sauer, R. M. (2007) · Oxford Review of Economic Policy

Open Source Software InnovationsDigital Platforms and Economics

Cited by 25

Wide-Sense Estimation on the Special Orthogonal Group

Chiuso, A., Picci, G., & Soatto, S. (2008) · Communications in Information and Systems

Target Tracking and Data Fusion in Sensor NetworksInertial Sensor and Navigation

Cited by 25

Comparison of sleep-wake classification using electroencephalogram and wrist-worn multi-modal sensor data

Sano, A., & Picard, R. W. (2014) · 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Context-Aware Activity Recognition SystemsEEG and Brain-Computer Interfaces

Cited by 25

Analysis of Cognitive Radio Spectrum Sensing Techniques over Nakagami-m Fading Channel

Shaikh, M. S., & Khare, P. K. (2020) · International Journal of Communications

Cognitive Radio Networks and Spectrum SensingWireless Signal Modulation Classification

Cited by 25

Automatic identification and segmentation of slice of minimal hiatal dimensions in transperineal ultrasound volumes

van den Noort, F., Manzini, C., van der Vaart, C. H., van Limbeek, M. A. J., Slump, C. H., & Grob, A. T. M. (2022) · Ultrasound in Obstetrics & Gynecology

Medical Image Segmentation TechniquesPelvic floor disorders treatments

Cited by 25

Differentially-Private "Draw and Discard" Machine Learning

Pihur, V., Korolova, A., Liu, F., Sankuratripati, S., Yung, M., Huang, D., & Zeng, R. (2018) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 25