Check computer science papers: citations, claims and retractions

PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition

Kong, Q., Cao, Y., Iqbal, T., Wang, Y., Wang, W., & Plumbley, M. D. (2020) · IEEE/ACM Transactions on Audio, Speech, and Language Processing

Music and Audio ProcessingSpeech Recognition and Synthesis

Cited by 1,235

CICIoT2023: A real-time dataset and benchmark for large-scale attacks in IoT environment

Neto, E. C. P., Dadkhah, S., Ferreira, R., Zohourian, A., Lu, R., & Ghorbani, A. A. (2023) · Sensors

Network Security and Intrusion DetectionIoT and Edge/Fog Computing

Cited by 1,235

Explaining machine learning classifiers through diverse counterfactual explanations

Mothilal, R. K., Sharma, A., & Tan, C. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 1,233

CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Hessel, J., Holtzman, A., Forbes, M., Le Bras, R., & Choi, Y. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Multimodal Machine Learning ApplicationsImage Retrieval and Classification Techniques

Cited by 1,232

Digital pathology and artificial intelligence

Niazi, M. K. K., Parwani, A. V., & Gurcan, M. N. (2019) · The Lancet Oncology

AI in cancer detectionArtificial Intelligence in Healthcare and Education

Cited by 1,232

A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications

Gui, J., Sun, Z., Wen, Y., Tao, D., & Ye, J. (2023) · IEEE Transactions on Knowledge and Data Engineering

Generative Adversarial Networks and Image SynthesisAdversarial Robustness in Machine Learning

Cited by 1,231

Systematic literature reviews in software engineering – A tertiary study

Kitchenham, B., Pretorius, R., Budgen, D., Pearl Brereton, O., Turner, M., Niazi, M., & Linkman, S. (2010) · Information and Software Technology

Software Engineering Techniques and PracticesMeta-analysis and systematic reviews

Cited by 1,230

Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems

Chen, T., & Chen, H. (1995) · IEEE Transactions on Neural Networks

Neural Networks and ApplicationsMachine Learning and ELM

Cited by 1,230

Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning

Norouzzadeh, M. S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M. S., Packer, C., & Clune, J. (2018) · Proceedings of the National Academy of Sciences

Advanced Neural Network ApplicationsWildlife Ecology and Conservation

Cited by 1,230

Evolution and Revolution in Artificial Intelligence in Education

Roll, I., & Wylie, R. (2016) · International Journal of Artificial Intelligence in Education

Intelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in Education

Cited by 1,229

Ethical principles for artificial intelligence in education

Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B.-P. T. (2023) · Education and Information Technologies

Ethics and Social Impacts of AIArtificial Intelligence in Education

Cited by 1,228

Machine Learning and Deep Learning Methods for Cybersecurity

Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., Gao, M., Hou, H., & Wang, C. (2018) · IEEE Access

Network Security and Intrusion DetectionNetwork Packet Processing and Optimization

Cited by 1,228

Focal Loss for Dense Object Detection

Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017) · arXiv

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 1,227

Class of quantum error-correcting codes saturating the quantum Hamming bound

Gottesman, D. (1996) · Physical Review A

Quantum Computing Algorithms and ArchitectureCoding theory and cryptography

Cited by 1,227

Unified Language Model Pre-training for Natural Language Understanding and Generation

Dong, L., Yang, N., Wang, W., Wei, F., Liu, X., Wang, Y., Gao, J., Zhou, M., & Hon, H.-W. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1,225

Bayesian surprise attracts human attention

Itti, L., & Baldi, P. (2009) · Vision Research

Visual Attention and Saliency DetectionVisual perception and processing mechanisms

Cited by 1,224

Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images

Sirinukunwattana, K., Raza, S. E. A., Tsang, Y.-W., Snead, D. R. J., Cree, I. A., & Rajpoot, N. M. (2016) · IEEE Transactions on Medical Imaging

AI in cancer detectionCell Image Analysis Techniques

Cited by 1,224

Fast Image Recovery Using Variable Splitting and Constrained Optimization

Afonso, M. V., Bioucas-Dias, J. M., & Figueiredo, M. A. T. (2010) · IEEE Transactions on Image Processing

Image and Signal Denoising MethodsSparse and Compressive Sensing Techniques

Cited by 1,223

If...Then: Algorithmic Power and Politics

Bucher, T. (2018)

Ethics and Social Impacts of AISocial Media and Politics

Cited by 1,223

Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric

Boughorbel, S., Jarray, F., & El-Anbari, M. (2017) · PLOS ONE

Imbalanced Data Classification TechniquesMachine Learning and Data Classification

Cited by 1,222

Toward Convolutional Blind Denoising of Real Photographs

Guo, S., Yan, Z., Zhang, K., Zuo, W., & Zhang, L. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Image and Signal Denoising MethodsImage Enhancement Techniques

Cited by 1,221

Information-based objective functions for active data selection

MacKay, D. J. C. (1992) · Neural Computation

Machine Learning and AlgorithmsGaussian Processes and Bayesian Inference

Cited by 1,221

chrF: character n-gram F-score for automatic MT evaluation

Popović, M. (2015) · Proceedings of the Tenth Workshop on Statistical Machine Translation

Natural Language Processing TechniquesSpeech Recognition and Synthesis

Cited by 1,220

A Survey of Quantization Methods for Efficient Neural Network Inference

Gholami, A., Kim, S., Dong, Z., Yao, Z., Mahoney, M. W., & Keutzer, K. (2022) · Low-Power Computer Vision

Advanced Neural Network ApplicationsNeural Networks and Applications

Cited by 1,219