Check computer science papers: citations, claims and retractions

Abstractions for network update

Reitblatt, M., Foster, N., Rexford, J., Schlesinger, C., & Walker, D. (2012) · Proceedings of the ACM SIGCOMM 2012 conference on Applications, technologies, architectures, and protocols for computer communication

Software-Defined Networks and 5GDistributed systems and fault tolerance

Cited by 584

Comparative analysis of blockchain consensus algorithms

Bach, L. M., Mihaljevic, B., & Zagar, M. (2018) · 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

Blockchain Technology Applications and SecurityBlockchain Technology in Education and Learning

Cited by 584

The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT

Wach, K., Duong, C. D., Ejdys, J., Kazlauskaitė, R., Korzynski, P., Mazurek, G., Paliszkiewicz, J., & Ziemba, E. (2023) · Entrepreneurial Business and Economics Review

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 584

Interpretable Machine Learning in Healthcare

Ahmad, M. A., Eckert, C., & Teredesai, A. (2018) · Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

Explainable Artificial Intelligence (XAI)Machine Learning in Healthcare

Cited by 583

Gated Feedback Recurrent Neural Networks

Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2015) · arXiv

Neural Networks and ApplicationsTopic Modeling

Cited by 583

The Impact of an Augmented Reality Application on Learning Motivation of Students

Khan, T., Johnston, K., & Ophoff, J. (2019) · Advances in Human-Computer Interaction

Augmented Reality ApplicationsVirtual Reality Applications and Impacts

Cited by 583

Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks

Goodfellow, I. J., Bulatov, Y., Ibarz, J., Arnoud, S., & Shet, V. (2013) · arXiv

Handwritten Text Recognition TechniquesVehicle License Plate Recognition

Cited by 582

Social Media Analytics and Intelligence

Zeng, D., Chen, H., Lusch, R., & Li, S.-H. (2010) · IEEE Intelligent Systems

Sentiment Analysis and Opinion MiningInformation Retrieval and Data Mining

Cited by 582

OpenAI Gym

Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., & Zaremba, W. (2016) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 582

Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network

Prasoon, A., Petersen, K., Igel, C., Lauze, F., Dam, E., & Nielsen, M. (2013) · Lecture Notes in Computer Science

Medical Image Segmentation TechniquesMedical Imaging and Analysis

Cited by 582

A model of inductive bias learning

Baxter, J. (2000) · Journal of Artificial Intelligence Research

Machine Learning and AlgorithmsDomain Adaptation and Few-Shot Learning

Cited by 582

Distributed denial of service attacks

Lau, F., Rubin, S. H., Smith, M. H., & Trajkovic, L. (2002) · SMC 2000 Conference Proceedings. 2000 IEEE International Conference on Systems, Man and Cybernetics. 'Cybernetics Evolving to Systems, Humans, Organizations, and their Complex Interactions' (Cat. No.00CH37166)

Network Security and Intrusion DetectionNetwork Traffic and Congestion Control

Cited by 582

Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers

Hohman, F., Kahng, M., Pienta, R., & Chau, D. H. (2019) · IEEE Transactions on Visualization and Computer Graphics

Data Visualization and AnalyticsExplainable Artificial Intelligence (XAI)

Cited by 582

Blockchains and the economic institutions of capitalism

DAVIDSON, S., DE FILIPPI, P., & POTTS, J. (2018) · Journal of Institutional Economics

Blockchain Technology Applications and SecurityEconomic Theory and Institutions

Cited by 582

Understanding intermediate layers using linear classifier probes

Alain, G., & Bengio, Y. (2016) · arXiv

Explainable Artificial Intelligence (XAI)Advanced Neural Network Applications

Cited by 581

Composition-based Multi-Relational Graph Convolutional Networks

Vashishth, S., Sanyal, S., Nitin, V., & Talukdar, P. (2019) · arXiv

Advanced Graph Neural NetworksGraph Theory and Algorithms

Cited by 581

Experimental Quantum Error Correction

Cory, D. G., Price, M. D., Maas, W., Knill, E., Laflamme, R., Zurek, W. H., Havel, T. F., & Somaroo, S. S. (1998) · Physical Review Letters

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 581

Automating string processing in spreadsheets using input-output examples

Gulwani, S. (2011) · ACM SIGPLAN Notices

Spreadsheets and End-User ComputingLogic, programming, and type systems

Cited by 581

Privacy-preserving artificial intelligence in healthcare: Techniques and applications

Khalid, N., Qayyum, A., Bilal, M., Al-Fuqaha, A., & Qadir, J. (2023) · Computers in Biology and Medicine

Privacy-Preserving Technologies in DataArtificial Intelligence in Healthcare and Education

Cited by 581

Learning task-dependent distributed representations by backpropagation through structure

Goller, C., & Kuchler, A. (2002) · Proceedings of International Conference on Neural Networks (ICNN'96)

Neural Networks and Applicationssemigroups and automata theory

Cited by 581

Detection and analysis of drive-by-download attacks and malicious JavaScript code

Cova, M., Kruegel, C., & Vigna, G. (2010) · Proceedings of the 19th international conference on World wide web

Web Application Security VulnerabilitiesAdvanced Malware Detection Techniques

Cited by 580

A Survey on Machine-Learning Techniques in Cognitive Radios

Bkassiny, M., Li, Y., & Jayaweera, S. K. (2013) · IEEE Communications Surveys & Tutorials

Cognitive Radio Networks and Spectrum SensingWireless Signal Modulation Classification

Cited by 580

A pathology foundation model for cancer diagnosis and prognosis prediction

Wang, X., Zhao, J., Marostica, E., Yuan, W., Jin, J., Zhang, J., Li, R., Tang, H., Wang, K., Li, Y., et al. (2024) · Nature

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 580

Adversarial training for free

Shafahi, A., Najibi, M., Ghiasi, A., Xu, Z., Dickerson, J., Studer, C., Davis, L. S., Taylor, G., & Goldstein, T. (2019) · ETH Zurich

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 580