Check computer science papers: citations, claims and retractions

Mistral 7B

Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., Casas, D. D. L., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 340

GDPR-Compliant Personal Data Management: A Blockchain-Based Solution

Truong, N. B., Sun, K., Lee, G. M., & Guo, Y. (2020) · IEEE Transactions on Information Forensics and Security

Blockchain Technology Applications and SecurityDigitalization, Law, and Regulation

Cited by 339

A Taxonomy and Survey of Edge Cloud Computing for Intelligent Transportation Systems and Connected Vehicles

Arthurs, P., Gillam, L., Krause, P., Wang, N., Halder, K., & Mouzakitis, A. (2022) · IEEE Transactions on Intelligent Transportation Systems

IoT and Edge/Fog ComputingVehicular Ad Hoc Networks (VANETs)

Cited by 339

Ring Confidential Transactions

Noether, S., Mackenzie, A., & Research Lab, T. M. (2016) · Ledger

Cryptography and Data SecurityBlockchain Technology Applications and Security

Cited by 339

PieAPP: Perceptual Image-Error Assessment Through Pairwise Preference

Prashnani, E., Cai, H., Mostofi, Y., & Sen, P. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Image and Video Quality AssessmentDigital Media Forensic Detection

Cited by 339

On the Fundamental Differences Between Interval Type-2 and Type-1 Fuzzy Logic Controllers

Wu, D. (2012) · IEEE Transactions on Fuzzy Systems

Fuzzy Logic and Control SystemsAdvanced Algebra and Logic

Cited by 339

An Intrusion Detection System Using a Deep Neural Network With Gated Recurrent Units

Xu, C., Shen, J., Du, X., & Zhang, F. (2018) · IEEE Access

Network Security and Intrusion DetectionNetwork Packet Processing and Optimization

Cited by 339

Towards a Big Data Curated Benchmark of Inter-project Code Clones

Svajlenko, J., Islam, J. F., Keivanloo, I., Roy, C. K., & Mia, M. M. (2014) · 2014 IEEE International Conference on Software Maintenance and Evolution

Software Engineering ResearchData Quality and Management

Cited by 339

Combating Deepfake Videos Using Blockchain and Smart Contracts

Hasan, H. R., & Salah, K. (2019) · IEEE Access

Blockchain Technology Applications and SecurityBlockchain Technology in Education and Learning

Cited by 339

Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon

Dong, X., Chen, S., & Pan, S. J. (2017) · arXiv

Advanced Neural Network ApplicationsMachine Learning and Data Classification

Cited by 339

FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic

van Ede, T., Bortolameotti, R., Continella, A., Ren, J., Dubois, D. J., Lindorfer, M., Choffnes, D., van Steen, M., & Peter, A. (2020) · Proceedings 2020 Network and Distributed System Security Symposium

Internet Traffic Analysis and Secure E-votingMobile and Web Applications

Cited by 339

FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces

Rössler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Nießner, M. (2018) · arXiv

Digital Media Forensic DetectionGenerative Adversarial Networks and Image Synthesis

Cited by 339

Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data

Itahara, S., Nishio, T., Koda, Y., Morikura, M., & Yamamoto, K. (2020) · arXiv

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

Cited by 338

Trust, transparency, and openness: How inclusion of cultural values shapes Nordic national public policy strategies for artificial intelligence (AI)

Robinson, S. C. (2020) · Technology in Society

Ethics and Social Impacts of AIE-Government and Public Services

Cited by 338

Diffusion Autoencoders: Toward a Meaningful and Decodable Representation

Preechakul, K., Chatthee, N., Wizadwongsa, S., & Suwajanakorn, S. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Generative Adversarial Networks and Image SynthesisImage and Signal Denoising Methods

Cited by 338

Distillation-based semi-supervised federated learning for communication-efficient collaborative training with non-IID private data

Itahara, S., Nishio, T., Koda, Y., Morikura, M., & Yamamoto, K. (2023) · IEEE Transactions on Mobile Computing

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

Cited by 338

Representation learning for dynamic graphs: A survey

Kazemi, S. M., Goel, R., Jain, K., Kobyzev, I., Sethi, A., Forsyth, P., & Poupart, P. (2019) · arXiv

Advanced Graph Neural NetworksGraph Theory and Algorithms

Cited by 338

Machine learning analysis of whole mouse brain vasculature

Todorov, M. I., Paetzold, J. C., Schoppe, O., Tetteh, G., Shit, S., Efremov, V., Todorov-Völgyi, K., Düring, M., Dichgans, M., Piraud, M., et al. (2020) · Nature Methods

Medical Image Segmentation TechniquesCell Image Analysis Techniques

Cited by 338

Gender and Dialect Bias in YouTube’s Automatic Captions

Tatman, R. (2017) · Proceedings of the First ACL Workshop on Ethics in Natural Language Processing

Speech Recognition and SynthesisLinguistic Variation and Morphology

Cited by 338

AI-IMU Dead-Reckoning

Brossard, M., Barrau, A., & Bonnabel, S. (2020) · IEEE Transactions on Intelligent Vehicles

Robotics and Sensor-Based LocalizationInertial Sensor and Navigation

Cited by 338

Deep Equilibrium Models

Bai, S., Kolter, J. Z., & Koltun, V. (2019) · arXiv

Topic ModelingNeural Networks and Applications

Cited by 338

Concept Bottleneck Models

Koh, P. W., Nguyen, T., Tang, Y. S., Mussmann, S., Pierson, E., Kim, B., & Liang, P. (2020) · arXiv

Explainable Artificial Intelligence (XAI)Machine Learning in Healthcare

Cited by 338

An Analysis of the Automatic Bug Fixing Performance of ChatGPT

Sobania, D., Briesch, M., Hanna, C., & Petke, J. (2023) · 2023 IEEE/ACM International Workshop on Automated Program Repair (APR)

Software Testing and Debugging TechniquesSoftware Engineering Research

Cited by 338

Subspace-search variational quantum eigensolver for excited states

Nakanishi, K. M., Mitarai, K., & Fujii, K. (2019) · Physical Review Research

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 337