Check computer science papers: citations, claims and retractions

A Logic Programming Language with Lambda-Abstraction, Function Variables, and Simple Unification

MILLER, D. (1991) · Journal of Logic and Computation

Logic, programming, and type systemsLogic, Reasoning, and Knowledge

Cited by 382

Few-Shot Incremental Learning with Continually Evolved Classifiers

Zhang, C., Song, N., Lin, G., Zheng, Y., Pan, P., & Xu, Y. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 382

Millisecond-scale molecular dynamics simulations on Anton

Shaw, D. E., Dror, R. O., Salmon, J. K., Grossman, J. P., Mackenzie, K. M., Bank, J. A., Young, C., Deneroff, M. M., Batson, B., Bowers, K. J., et al. (2009) · Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis

Parallel Computing and Optimization TechniquesProtein Structure and Dynamics

Cited by 382

Progressive Domain Adaptation for Object Detection

Hsu, H.-K., Yao, C.-H., Tsai, Y.-H., Hung, W.-C., Tseng, H.-Y., Singh, M., & Yang, M.-H. (2020) · 2020 IEEE Winter Conference on Applications of Computer Vision (WACV)

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 382

Pathology image analysis using segmentation deep learning algorithms

Wang, S., Yang, D. M., Rong, R., Zhan, X., & Xiao, G. (2019) · The American Journal of Pathology

AI in cancer detectionMedical Image Segmentation Techniques

Cited by 382

Use chat gpt to solve programming bugs

Surameery, N. M. S., & Shakor, M. Y. (2023) · International Journal of Information technology and Computer Engineering

Software Engineering ResearchSoftware Testing and Debugging Techniques

Cited by 382

Improving language models by retrieving from trillions of tokens

Borgeaud, S., Mensch, A., Hoffmann, J., Cai, T., Rutherford, E., Millican, K., Driessche, G. V. D., Lespiau, J.-B., Damoc, B., Clark, A., et al. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 382

Emotion Sensors Go To School

Ivon, A., Cooper David, G., Winslow, B., Park, W. B., Kasia, M., & Robert, C. (2009) · Frontiers in Artificial Intelligence and Applications

Intelligent Tutoring Systems and Adaptive LearningEmotion and Mood Recognition

Cited by 382

A comparison of features for synthetic speech detection

Sahidullah, M., Kinnunen, T., & Hanilçi, C. (2015) · Interspeech 2015

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 382

Temporal Graph Networks for Deep Learning on Dynamic Graphs

Rossi, E., Chamberlain, B., Frasca, F., Eynard, D., Monti, F., & Bronstein, M. (2020) · arXiv

Advanced Graph Neural NetworksGraph Theory and Algorithms

Cited by 382

Psycho-physiological measures for assessing cognitive load

Haapalainen, E., Kim, S., Forlizzi, J. F., & Dey, A. K. (2010) · Proceedings of the 12th ACM international conference on Ubiquitous computing

Emotion and Mood RecognitionHuman-Automation Interaction and Safety

Cited by 382

Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores

Hussain, M., Zhu, W., Zhang, W., & Abidi, S. M. R. (2018) · Computational Intelligence and Neuroscience

Online Learning and AnalyticsIntelligent Tutoring Systems and Adaptive Learning

Cited by 382

Blockchain applications for industry 4.0 and industrial IoT: a review

Alladi, T., Chamola, V., Parizi, R. M., & Choo, K.-K. R. (2019) · IEEE Access

Blockchain Technology Applications and SecurityBlockchain Technology in Education and Learning

Cited by 382

Computational thinking for teacher education

Yadav, A., Stephenson, C., & Hong, H. (2017) · Communications of the ACM

Teaching and Learning ProgrammingMathematics Education and Teaching Techniques

Cited by 382

AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients

Zhuang, J., Tang, T., Ding, Y., Tatikonda, S., Dvornek, N., Papademetris, X., & Duncan, J. S. (2020) · arXiv

Stochastic Gradient Optimization TechniquesMachine Learning and Data Classification

Cited by 382

Quantum-assisted quantum compiling

Khatri, S., LaRose, R., Poremba, A., Cincio, L., Sornborger, A. T., & Coles, P. J. (2019) · Quantum

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 382

Relative to a Random Oracle A , ${\bf P}^A \ne {\bf NP}^A \ne \text{co-}{\bf NP}^A $ with Probability 1

Bennett, C. H., & Gill, J. (1981) · SIAM Journal on Computing

Complexity and Algorithms in GraphsComputability, Logic, AI Algorithms

Cited by 381

Structured Attention Networks

Kim, Y., Denton, C., Hoang, L., & Rush, A. M. (2017) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 381

Don’t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases

Clark, C., Yatskar, M., & Zettlemoyer, L. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 381

Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning

Shi, W., Zhou, S., Niu, Z., Jiang, M., & Geng, L. (2021) · IEEE Transactions on Wireless Communications

Privacy-Preserving Technologies in DataAdvanced Wireless Network Optimization

Cited by 381

Discrete-Continuous Depth Estimation from a Single Image

Liu, M., Salzmann, M., & He, X. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition

Advanced Vision and ImagingImage Processing Techniques and Applications

Cited by 381

ZeroQ: A Novel Zero Shot Quantization Framework

Cai, Y., Yao, Z., Dong, Z., Gholami, A., Mahoney, M. W., & Keutzer, K. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 381

Improving Federated Learning Personalization via Model Agnostic Meta Learning

Jiang, Y., Konečný, J., Rush, K., & Kannan, S. (2019) · arXiv

Privacy-Preserving Technologies in DataDomain Adaptation and Few-Shot Learning

Cited by 381

The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race

Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2017) · Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion

Spam and Phishing DetectionMisinformation and Its Impacts

Cited by 381