Check computer science papers: citations, claims and retractions

A clinical benchmark of public self-supervised pathology foundation models

Campanella, G., Chen, S., Singh, M., Verma, R., Muehlstedt, S., Zeng, J., Stock, A., Croken, M., Veremis, B., Elmas, A., et al. (2025) · Nature Communications

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 126

Routing for Crowd Management in Smart Cities: A Deep Reinforcement Learning Perspective

Zhao, L., Wang, J., Liu, J., & Kato, N. (2019) · IEEE Communications Magazine

Software-Defined Networks and 5GIoT and Edge/Fog Computing

Cited by 126

Deep Activity Recognition Models with Triaxial Accelerometers

Alsheikh, M. A., Selim, A., Niyato, D., Doyle, L., Lin, S., & Tan, H.-P. (2015) · arXiv

Context-Aware Activity Recognition SystemsHuman Mobility and Location-Based Analysis

Cited by 126

Dynamic Model Pruning with Feedback

Lin, T., Stich, S. U., Barba, L., Dmitriev, D., & Jaggi, M. (2020) · arXiv

Advanced Neural Network ApplicationsMachine Learning and Data Classification

Cited by 126

A General Approach to Adding Differential Privacy to Iterative Training Procedures

McMahan, H. B., Andrew, G., Erlingsson, U., Chien, S., Mironov, I., Papernot, N., & Kairouz, P. (2018) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 126

Activity recognition from on-body sensors by classifier fusion: sensor scalability and robustness

Zappi, P., Stiefmeier, T., Farella, E., Roggen, D., Benini, L., & Troster, G. (2007) · 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information

Context-Aware Activity Recognition SystemsWireless Body Area Networks

Cited by 126

Learning to Summarize Radiology Findings

Zhang, Y., Ding, D. Y., Qian, T., Manning, C. D., & Langlotz, C. P. (2018) · arXiv

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 126

Micro-Batch Training with Batch-Channel Normalization and Weight Standardization

Qiao, S., Wang, H., Liu, C., Shen, W., & Yuille, A. (2019) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 126

Optimal layout synthesis for quantum computing

Tan, B., & Cong, J. (2020) · Proceedings of the 39th International Conference on Computer-Aided Design

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 126

Stabilizing Differentiable Architecture Search via Perturbation-based Regularization

Chen, X., & Hsieh, C.-J. (2020) · arXiv

Advanced Neural Network ApplicationsMachine Learning and Data Classification

Cited by 126

Privacy-preserving neural networks with Homomorphic encryption: Challenges and opportunities

Pulido-Gaytan, B., Tchernykh, A., Cortés-Mendoza, J. M., Babenko, M., Radchenko, G., Avetisyan, A., & Drozdov, A. Y. (2021) · Peer-to-Peer Networking and Applications

Cryptography and Data SecurityCloud Data Security Solutions

Cited by 126

General Conditions for Approximate Quantum Error Correction and Near-Optimal Recovery Channels

Bény, C., & Oreshkov, O. (2010) · Physical Review Letters

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 126

Learning to Summarize Radiology Findings

Zhang, Y., Ding, D. Y., Qian, T., Manning, C. D., & Langlotz, C. P. (2018) · Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 126

An Intelligent Trust Cloud Management Method for Secure Clustering in 5G Enabled Internet of Medical Things

Yang, L., Yu, K., Yang, S. X., Chakraborty, C., Lu, Y., & Guo, T. (2022) · IEEE Transactions on Industrial Informatics

IoT and Edge/Fog ComputingAccess Control and Trust

Cited by 126

Fine-tuning large language models for domain adaptation: exploration of training strategies, scaling, model merging and synergistic capabilities

Lu, W., Luu, R. K., & Buehler, M. J. (2025) · npj Computational Materials

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 126

Edge Intelligence: Challenges and Opportunities of Near-Sensor Machine Learning Applications

Plastiras, G., Terzi, M., Kyrkou, C., & Theocharides, T. (2018) · 2018 IEEE 29th International Conference on Application-specific Systems, Architectures and Processors (ASAP)

IoT and Edge/Fog ComputingInternet of Things and AI

Cited by 126

Statistical selection of relevant subspace projections for outlier ranking

Muller, E., Schiffer, M., & Seidl, T. (2011) · 2011 IEEE 27th International Conference on Data Engineering

Anomaly Detection Techniques and ApplicationsAdvanced Statistical Methods and Models

Cited by 126

Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning

Jhunjhunwala, D., Gadhikar, A., Joshi, G., & Eldar, Y. C. (2021) · ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 126

An e-learning approach to secondary education in Palestine: opportunities and challenges

Shraim, K., & Khlaif, Z. (2010) · Information Technology for Development

Educational Technology and E-LearningTechnology-Enhanced Education Studies

Cited by 126

Video-based raindrop detection for improved image registration

Roser, M., & Geiger, A. (2009) · 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops

Image Enhancement TechniquesImage and Video Stabilization

Cited by 126

Cyberthreat Detection from Twitter using Deep Neural Networks

Dionisio, N., Alves, F., Ferreira, P. M., & Bessani, A. (2019) · 2019 International Joint Conference on Neural Networks (IJCNN)

Network Security and Intrusion DetectionText and Document Classification Technologies

Cited by 126

Accurately computing the log-sum-exp and softmax functions

Blanchard, P., Higham, D. J., & Higham, N. J. (2021) · IMA Journal of Numerical Analysis

Numerical Methods and AlgorithmsMathematical and Computational Methods

Cited by 126

Dynamics of Facial Expression Extracted Automatically from Video

Littlewort, G., Bartlett, M. S., Fasel, I., Susskind, J., & Movellan, J. (2005) · 2004 Conference on Computer Vision and Pattern Recognition Workshop

Emotion and Mood RecognitionFace recognition and analysis

Cited by 126

The pipeline for the continuous development of artificial intelligence models—Current state of research and practice

Steidl, M., Felderer, M., & Ramler, R. (2023) · Journal of Systems and Software

Software Engineering Techniques and PracticesSoftware Engineering Research

Cited by 126