Check computer science papers: citations, claims and retractions

Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality

Dell'Acqua, F., McFowland III, E., Mollick, E. R., Lifshitz-Assaf, H., Kellogg, K., Rajendran, S., Krayer, L., Candelon, F., & Lakhani, K. R. (2023) · SSRN Electronic Journal

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

Cited by 794

Visual7W: Grounded Question Answering in Images

Zhu, Y., Groth, O., Bernstein, M., & Fei-Fei, L. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsVisual Attention and Saliency Detection

Cited by 794

A New Representation of Skeleton Sequences for 3D Action Recognition

Ke, Q., Bennamoun, M., An, S., Sohel, F., & Boussaid, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Human Pose and Action RecognitionHand Gesture Recognition Systems

Cited by 793

Geometric Manipulation of Trapped Ions for Quantum Computation

Duan, L.-M., Cirac, J. I., & Zoller, P. (2001) · Science

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 793

DISFA: A Spontaneous Facial Action Intensity Database

Mavadati, S. M., Mahoor, M. H., Bartlett, K., Trinh, P., & Cohn, J. F. (2013) · IEEE Transactions on Affective Computing

Emotion and Mood RecognitionFace recognition and analysis

Cited by 793

AI models collapse when trained on recursively generated data

Shumailov, I., Shumaylov, Z., Zhao, Y., Papernot, N., Anderson, R., & Gal, Y. (2024) · Nature

Topic ModelingEthics and Social Impacts of AI

Cited by 793

Artificial intelligence in the creative industries: a review

Anantrasirichai, N., & Bull, D. (2022) · Artificial Intelligence Review

Generative Adversarial Networks and Image SynthesisArtificial Intelligence Applications

Cited by 793

Learning Accurate, Compact, and Interpretable Tree Annotation

Petrov, S., Barrett, L., Thibaux, R., & Klein, D. (2006) · Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06

Natural Language Processing TechniquesSyntax, Semantics, Linguistic Variation

Cited by 793

Orthogonal defect classification-a concept for in-process measurements

Chillarege, R., Bhandari, I. S., Chaar, J. K., Halliday, M. J., Moebus, D. S., Ray, B. K., & Wong, M.-Y. (1992) · IEEE Transactions on Software Engineering

Software Engineering Techniques and PracticesSoftware Engineering Research

Cited by 792

Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review

Xiao, C., Choi, E., & Sun, J. (2018) · Journal of the American Medical Informatics Association

Machine Learning in HealthcareElectronic Health Records Systems

Cited by 792

User experience (UX): towards an experiential perspective on product quality

Hassenzahl, M. (2008) · Proceedings of the 20th Conference on l'Interaction Homme-Machine

Innovative Human-Technology InteractionUsability and User Interface Design

Cited by 791

Optimization of sparse matrix-vector multiplication on emerging multicore platforms

Williams, S., Oliker, L., Vuduc, R., Shalf, J., Yelick, K., & Demmel, J. (2007) · Proceedings of the 2007 ACM/IEEE conference on Supercomputing

Parallel Computing and Optimization TechniquesMatrix Theory and Algorithms

Cited by 791

Toward an affect-sensitive multimodal human-computer interaction

Pantic, M., & Rothkrantz, L. J. M. (2003) · Proceedings of the IEEE

Emotion and Mood RecognitionInnovative Human-Technology Interaction

Cited by 791

Leakage in data mining: Formulation, detection, and avoidance

Kaufman, S., Rosset, S., Perlich, C., & Stitelman, O. (2012) · ACM Transactions on Knowledge Discovery from Data

Machine Learning and Data ClassificationData Mining and Machine Learning Applications

Cited by 791

Detecting spammers on social networks

Stringhini, G., Kruegel, C., & Vigna, G. (2010) · Proceedings of the 26th Annual Computer Security Applications Conference

Spam and Phishing DetectionMisinformation and Its Impacts

Cited by 791

Noise-Induced Barren Plateaus in Variational Quantum Algorithms

Wang, S., Fontana, E., Cerezo, M., Sharma, K., Sone, A., Cincio, L., & Coles, P. J. (2020) · arXiv

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 791

A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay

Smith, L. N. (2018) · arXiv

Stochastic Gradient Optimization TechniquesMachine Learning and Data Classification

Cited by 790

Globally-optimal greedy algorithms for tracking a variable number of objects

Pirsiavash, H., Ramanan, D., & Fowlkes, C. C. (2011) · CVPR 2011

Video Surveillance and Tracking MethodsAutonomous Vehicle Technology and Safety

Cited by 790

Do ImageNet Classifiers Generalize to ImageNet?

Recht, B., Roelofs, R., Schmidt, L., & Shankar, V. (2019) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 790

How Attentive are Graph Attention Networks?

Brody, S., Alon, U., & Yahav, E. (2021) · arXiv

Advanced Graph Neural NetworksGraph Theory and Algorithms

Cited by 789

Waterloo Exploration Database: New Challenges for Image Quality Assessment Models

Ma, K., Duanmu, Z., Wu, Q., Wang, Z., Yong, H., Li, H., & Zhang, L. (2017) · IEEE Transactions on Image Processing

Image and Video Quality AssessmentDigital Media Forensic Detection

Cited by 789

Collecting complex activity datasets in highly rich networked sensor environments

Roggen, D., Calatroni, A., Rossi, M., Holleczek, T., Forster, K., Troster, G., Lukowicz, P., Bannach, D., Pirkl, G., Ferscha, A., et al. (2010) · 2010 Seventh International Conference on Networked Sensing Systems (INSS)

Context-Aware Activity Recognition SystemsWireless Sensor Networks for Data Analysis

Cited by 788

Smart contract-based access control for the internet of things

Zhang, Y., Kasahara, S., Shen, Y., Jiang, X., & Wan, J. (2019) · IEEE Internet of Things Journal

Blockchain Technology Applications and SecurityAccess Control and Trust

Cited by 788

How to learn and how to teach computational thinking: Suggestions based on a review of the literature

Hsu, T.-C., Chang, S.-C., & Hung, Y.-T. (2018) · Computers & Education

Teaching and Learning ProgrammingProblem and Project Based Learning

Cited by 788