Check computer science papers: citations, claims and retractions

A Survey of Uncertainty in Deep Neural Networks

Gawlikowski, J., Tassi, C. R. N., Ali, M., Lee, J., Humt, M., Feng, J., Kruspe, A., Triebel, R., Jung, P., Roscher, R., et al. (2021) · arXiv

Adversarial Robustness in Machine LearningAdvanced Neural Network Applications

Cited by 232

Interpretable survival prediction for colorectal cancer using deep learning

Wulczyn, E., Steiner, D. F., Moran, M., Plass, M., Reihs, R., Tan, F., Flament-Auvigne, I., Brown, T., Regitnig, P., Chen, P.-H. C., et al. (2021) · npj Digital Medicine

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 231

A deep reinforcement learning-based framework for content caching

Zhong, C., Gursoy, M. C., & Velipasalar, S. (2018) · 2018 52nd Annual Conference on Information Sciences and Systems (CISS)

Caching and Content DeliveryIoT and Edge/Fog Computing

Cited by 231

Federated Learning for Emoji Prediction in a Mobile Keyboard

Ramaswamy, S., Mathews, R., Rao, K., & Beaufays, F. (2019) · arXiv

Privacy-Preserving Technologies in DataDigital Communication and Language

Cited by 231

Applying intrusion detection systems to wireless sensor networks

Roman, R., Zhou, J., & Lopez, J. (2006) · CCNC 2006. 2006 3rd IEEE Consumer Communications and Networking Conference, 2006.

Security in Wireless Sensor NetworksNetwork Security and Intrusion Detection

Cited by 231

IoT and Fog-Computing-Based Predictive Maintenance Model for Effective Asset Management in Industry 4.0 Using Machine Learning

Teoh, Y. K., Gill, S. S., & Parlikad, A. K. (2023) · IEEE Internet of Things Journal

IoT and Edge/Fog ComputingDigital Transformation in Industry

Cited by 231

Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees

Celis, L. E., Huang, L., Keswani, V., & Vishnoi, N. K. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIPrivacy-Preserving Technologies in Data

Cited by 231

Adaptive and Intelligent Systems for Collaborative Learning Support: A Review of the Field

Magnisalis, I., Demetriadis, S., & Karakostas, A. (2011) · IEEE Transactions on Learning Technologies

Intelligent Tutoring Systems and Adaptive LearningInnovative Teaching and Learning Methods

Cited by 231

An Empirical Study of RefactoringChallenges and Benefits at Microsoft

Kim, M., Zimmermann, T., & Nagappan, N. (2014) · IEEE Transactions on Software Engineering

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 231

Pathologist-level classification of histopathological melanoma images with deep neural networks

Hekler, A., Utikal, J. S., Enk, A. H., Berking, C., Klode, J., Schadendorf, D., Jansen, P., Franklin, C., Holland-Letz, T., Krahl, D., et al. (2019) · European Journal of Cancer

AI in cancer detectionCutaneous Melanoma Detection and Management

Cited by 231

A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives

Mathis, A., Schneider, S., Lauer, J., & Mathis, M. W. (2020) · Neuron

Human Pose and Action RecognitionHuman Motion and Animation

Cited by 231

Learning a better representation of speech soundwaves using restricted boltzmann machines

Jaitly, N., & Hinton, G. (2011) · 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 231

Less training, more repairing please: revisiting automated program repair via zero-shot learning

Xia, C. S., & Zhang, L. (2022) · Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Software Testing and Debugging TechniquesSoftware Engineering Research

Cited by 231

Improving Neural Network Quantization without Retraining using Outlier Channel Splitting

Zhao, R., Hu, Y., Dotzel, J., De Sa, C., & Zhang, Z. (2019) · arXiv

Advanced Neural Network ApplicationsParallel Computing and Optimization Techniques

Cited by 231

Quantum Attacks on Bitcoin, and How to Protect Against Them

Aggarwal, D., Brennen, G., Lee, T., Santha, M., & Tomamichel, M. (2018) · Ledger

Blockchain Technology Applications and SecurityCryptography and Data Security

Cited by 231

Fifty Years of Research on Self-Replication: An Overview

Sipper, M. (1998) · Artificial Life

Cellular Automata and ApplicationsComputability, Logic, AI Algorithms

Cited by 231

Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals

Van Gansbeke, W., Vandenhende, S., Georgoulis, S., & Van Gool, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 231

Vision–language foundation model for echocardiogram interpretation

Christensen, M., Vukadinovic, M., Yuan, N., & Ouyang, D. (2024) · Nature Medicine

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 231

Eviza: A Natural Language Interface for Visual Analysis

Setlur, V., Battersby, S. E., Tory, M., Gossweiler, R., & Chang, A. X. (2016) · Proceedings of the 29th Annual Symposium on User Interface Software and Technology

Data Visualization and AnalyticsNatural Language Processing Techniques

Cited by 231

Conditions for effective smart learning environments

Koper, R. (2014) · Smart Learning Environments

Intelligent Tutoring Systems and Adaptive LearningInnovative Teaching and Learning Methods

Cited by 231

High-performance combinatorial optimization based on classical mechanics

Goto, H., Endo, K., Suzuki, M., Sakai, Y., Kanao, T., Hamakawa, Y., Hidaka, R., Yamasaki, M., & Tatsumura, K. (2021) · Science Advances

Quantum Computing Algorithms and ArchitectureMetaheuristic Optimization Algorithms Research

Cited by 231

Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speech

Huang, F., Kwak, H., & An, J. (2023) · arXiv

Hate Speech and Cyberbullying DetectionExplainable Artificial Intelligence (XAI)

Cited by 231

Using neuroscience to develop artificial intelligence

Ullman, S. (2019) · Science

Neural Networks and ApplicationsCognitive Computing and Networks

Cited by 231

Visual Programming: Compositional visual reasoning without training

Gupta, T., & Kembhavi, A. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsLogic, Reasoning, and Knowledge

Cited by 230