Check computer science papers: citations, claims and retractions

Federated Machine Learning: Concept and Applications

Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology

Privacy-Preserving Technologies in DataCryptography and Data Security

Cited by 6,322

Show and tell: A neural image caption generator

Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsImage Retrieval and Classification Techniques

Cited by 6,318

Shape matching and object recognition using shape contexts

Belongie, S., Malik, J., & Puzicha, J. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Image Retrieval and Classification TechniquesAdvanced Image and Video Retrieval Techniques

Cited by 6,306

Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

Adadi, A., & Berrada, M. (2018) · IEEE Access

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 6,294

Random early detection gateways for congestion avoidance

Floyd, S., & Jacobson, V. (1993) · IEEE/ACM Transactions on Networking

Network Traffic and Congestion ControlSoftware-Defined Networks and 5G

Cited by 6,290

A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge.

Landauer, T. K., & Dumais, S. T. (1997) · Psychological Review

Topic ModelingReading and Literacy Development

Cited by 6,282

Abstract interpretation: a unified lattice model for static analysis of programs by construction or approximation of fixpoints

Cousot, P., & Cousot, R. (1977) · Proceedings of the 4th ACM SIGACT-SIGPLAN symposium on Principles of programming languages - POPL '77

Logic, programming, and type systemsFormal Methods in Verification

Cited by 6,279

CBAM: Convolutional Block Attention Module

Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018) · arXiv

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 6,261

A survey of transfer learning

Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016) · Journal of Big Data

Domain Adaptation and Few-Shot LearningMachine Learning and Algorithms

Cited by 6,260

Multiscale structural similarity for image quality assessment

Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2004) · The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003

Image and Video Quality AssessmentImage Retrieval and Classification Techniques

Cited by 6,253

Fog computing and its role in the internet of things

Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012) · Proceedings of the first edition of the MCC workshop on Mobile cloud computing

IoT and Edge/Fog ComputingEnergy Efficiency in Computing

Cited by 6,244

Representation Learning with Contrastive Predictive Coding

Oord, A. V. D., Li, Y., & Vinyals, O. (2018) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 6,242

Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks

Zhang, K., Zhang, Z., Li, Z., & Qiao, Y. (2016) · IEEE Signal Processing Letters

Face recognition and analysisAdvanced Neural Network Applications

Cited by 6,214

Federated learning: Challenges, methods, and future directions

Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020) · IEEE Signal Processing Magazine

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 6,210

SQuAD: 100,000+ Questions for Machine Comprehension of Text

Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 6,208

Estimating the Number of Clusters in a Data Set Via the Gap Statistic

Tibshirani, R., Walther, G., & Hastie, T. (2001) · Journal of the Royal Statistical Society Series B: Statistical Methodology

Advanced Clustering Algorithms ResearchBayesian Methods and Mixture Models

Cited by 6,205

SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 6,201

Estimating the Support of a High-Dimensional Distribution

Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001) · Neural Computation

Machine Learning and AlgorithmsNeural Networks and Applications

Cited by 6,168

Cortical Surface-Based Analysis

Fischl, B., Sereno, M. I., & Dale, A. M. (1999) · NeuroImage

Computer Graphics and Visualization TechniquesAdvanced Neuroimaging Techniques and Applications

Cited by 6,147

The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

Chicco, D., & Jurman, G. (2020) · BMC Genomics

Imbalanced Data Classification TechniquesMachine Learning and Data Classification

Cited by 6,143

Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Xiao, H., Rasul, K., & Vollgraf, R. (2017) · arXiv

Advanced Neural Network ApplicationsMachine Learning and Data Classification

Cited by 6,108

VADER: a parsimonious rule-based model for sentiment analysis of social media text

Hutto, C., & Gilbert, E. (2014) · Proceedings of the International AAAI Conference on Web and Social Media

Sentiment Analysis and Opinion MiningEmotion and Mood Recognition

Cited by 6,087

The Laplacian pyramid as a compact image code

Burt, P., & Adelson, E. (1983) · IEEE Transactions on Communications

Advanced Data Compression TechniquesImage Retrieval and Classification Techniques

Cited by 6,077

“Librispeech: an ASR corpus based on public domain audio books,”

Panayotov, V., Chen, G., Povey, D., & Khudanpur, S. (2015) · 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 6,072