Check computer science papers: citations, claims and retractions

Importance Weighted Autoencoders

Burda, Y., Grosse, R., & Salakhutdinov, R. (2015) · arXiv

Gaussian Processes and Bayesian InferenceTopic Modeling

Cited by 644

MirrorGAN: Learning Text-To-Image Generation by Redescription

Qiao, T., Zhang, J., Xu, D., & Tao, D. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 644

JAABA: Interactive machine learning for automatic annotation of animal behavior

Kabra, M., Robie, A. A., Rivera-Alba, M., Branson, S., & Branson, K. (2013) · Nature Methods

Human Pose and Action RecognitionCell Image Analysis Techniques

Cited by 644

Deep learning based tissue analysis predicts outcome in colorectal cancer

Bychkov, D., Linder, N., Turkki, R., Nordling, S., Kovanen, P. E., Verrill, C., Walliander, M., Lundin, M., Haglund, C., & Lundin, J. (2018) · Scientific Reports

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 644

Role of fairness, accountability, and transparency in algorithmic affordance

Shin, D., & Park, Y. J. (2019) · Computers in Human Behavior

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 643

StereoSet: Measuring stereotypical bias in pretrained language models

Nadeem, M., Bethke, A., & Reddy, S. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Ethics and Social Impacts of AIHate Speech and Cyberbullying Detection

Cited by 643

StereoSet: Measuring stereotypical bias in pretrained language models

Nadeem, M., Bethke, A., & Reddy, S. (2020) · arXiv

Ethics and Social Impacts of AIHate Speech and Cyberbullying Detection

Cited by 643

Unified Pre-training for Program Understanding and Generation

Ahmad, W., Chakraborty, S., Ray, B., & Chang, K.-W. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Software Engineering ResearchTopic Modeling

Cited by 642

Universal Adversarial Triggers for Attacking and Analyzing NLP

Wallace, E., Feng, S., Kandpal, N., Gardner, M., & Singh, S. (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)

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 642

Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing

von Krogh, G. (2018) · Academy of Management Discoveries

Artificial Intelligence ApplicationsImpact of AI and Big Data on Business and Society

Cited by 641

Artificial intelligence: a survey on evolution, models, applications and future trends

Lu, Y. (2019) · Journal of Management Analytics

Artificial Intelligence ApplicationsInternet of Things and AI

Cited by 641

The optimal template effect in hippocampus studies of diseased populations

Avants, B. B., Yushkevich, P., Pluta, J., Minkoff, D., Korczykowski, M., Detre, J., & Gee, J. C. (2010) · NeuroImage

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 640

Learning to generate chairs with convolutional neural networks

Dosovitskiy, A., Springenberg, J. T., & Brox, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

3D Shape Modeling and AnalysisGenerative Adversarial Networks and Image Synthesis

Cited by 640

MSM: a new flexible framework for Multimodal Surface Matching

Robinson, E. C., Jbabdi, S., Glasser, M. F., Andersson, J., Burgess, G. C., Harms, M. P., Smith, S. M., Van Essen, D. C., & Jenkinson, M. (2014) · NeuroImage

Medical Image Segmentation TechniquesFunctional Brain Connectivity Studies

Cited by 640

Scheduling Policies for Federated Learning in Wireless Networks

Yang, H. H., Liu, Z., Quek, T. Q. S., & Poor, H. V. (2020) · IEEE Transactions on Communications

Privacy-Preserving Technologies in DataAdvanced Wireless Network Optimization

Cited by 640

Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU

Lee, V. W., Kim, C., Chhugani, J., Deisher, M., Kim, D., Nguyen, A. D., Satish, N., Smelyanskiy, M., Chennupaty, S., Hammarlund, P., et al. (2010) · Proceedings of the 37th annual international symposium on Computer architecture

Parallel Computing and Optimization TechniquesComputer Graphics and Visualization Techniques

Cited by 640

Failure rates in introductory programming revisited

Watson, C., & Li, F. W. B. (2014) · Proceedings of the 2014 conference on Innovation & technology in computer science education - ITiCSE '14

Teaching and Learning ProgrammingStatistics Education and Methodologies

Cited by 639

Efficient large-scale language model training on GPU clusters using megatron-LM

Narayanan, D., Shoeybi, M., Casper, J., LeGresley, P., Patwary, M., Korthikanti, V., Vainbrand, D., Kashinkunti, P., Bernauer, J., Catanzaro, B., et al. (2021) · Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

Parallel Computing and Optimization TechniquesTopic Modeling

Cited by 639

The graphical presentation of a collection of means

Goldstein, H., & Healy, M. J. R. (1995) · Journal of the Royal Statistical Society. Series A (Statistics in Society)

Data Visualization and AnalyticsStatistical Methods and Applications

Cited by 639

Neural Network for Graphs: A Contextual Constructive Approach

Micheli, A. (2009) · IEEE Transactions on Neural Networks

Neural Networks and ApplicationsAdvanced Graph Neural Networks

Cited by 639

Design Guidelines for Prompt Engineering Text-to-Image Generative Models

Liu, V., & Chilton, L. B. (2022) · CHI Conference on Human Factors in Computing Systems

Generative Adversarial Networks and Image SynthesisDigital Media and Visual Art

Cited by 639

Quantum lower bounds by polynomials

Beals, R., Buhrman, H., Cleve, R., Mosca, M., & de Wolf, R. (2001) · Journal of the ACM

Quantum Computing Algorithms and ArchitectureComplexity and Algorithms in Graphs

Cited by 639

Wedgelets: nearly minimax estimation of edges

Donoho, D. L. (1999) · The Annals of Statistics

Image and Signal Denoising MethodsStatistical Methods and Inference

Cited by 638

The power of generative AI: A review of requirements, models, input–output formats, evaluation metrics, and challenges

Bandi, A., Adapa, P. V. S. R., & Kuchi, Y. E. V. P. K. (2023) · Future Internet

Generative Adversarial Networks and Image SynthesisArtificial Intelligence Applications

Cited by 638