Check computer science papers: citations, claims and retractions

The mythos of model interpretability

Lipton, Z. C. (2018) · Communications of the ACM

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

Cited by 5,427

The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery.

Lipton, Z. C. (2018) · Queue

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

Cited by 5,427

The Mythos of Model Interpretability

Lipton, Z. C. (2016) · arXiv

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

Cited by 5,422

LINE: Large-scale Information Network Embedding

Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., & Mei, Q. (2015) · Proceedings of the 24th International Conference on World Wide Web

Advanced Graph Neural NetworksComplex Network Analysis Techniques

Cited by 5,416

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

Yan, S., Xiong, Y., & Lin, D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence

Human Pose and Action RecognitionHand Gesture Recognition Systems

Cited by 5,410

Active appearance models

Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Medical Image Segmentation TechniquesFace recognition and analysis

Cited by 5,409

Amazon.com recommendations: item-to-item collaborative filtering

Linden, G., Smith, B., & York, J. (2003) · IEEE Internet Computing

Recommender Systems and TechniquesInformation Retrieval and Search Behavior

Cited by 5,403

CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features

Yun, S., Han, D., Chun, S., Oh, S. J., Yoo, Y., & Choe, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 5,400

A detailed analysis of the KDD CUP 99 data set

Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009) · 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications

Network Security and Intrusion DetectionArtificial Immune Systems Applications

Cited by 5,396

Recurrent neural network based language model

Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010

Speech Recognition and SynthesisNatural Language Processing Techniques

Cited by 5,390

Adaptive Mixtures of Local Experts

Jacobs, R. A., Jordan, M. I., Nowlan, S. J., & Hinton, G. E. (1991) · Neural Computation

Neural Networks and ApplicationsSpeech Recognition and Synthesis

Cited by 5,384

Error Detecting and Error Correcting Codes

Hamming, R. W. (1950) · Bell System Technical Journal

Coding theory and cryptographyError Correcting Code Techniques

Cited by 5,377

Adversarial examples in the physical world

Kurakin, A., Goodfellow, I. J., & Bengio, S. (2018) · Artificial Intelligence Safety and Security

Adversarial Robustness in Machine LearningDigital Media Forensic Detection

Cited by 5,375

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv

Advanced Neural Network ApplicationsExplainable Artificial Intelligence (XAI)

Cited by 5,370

Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks

Graves, A., Fernández, S., Gomez, F., & Schmidhuber, J. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 5,370

Curriculum learning

Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning

Stochastic Gradient Optimization TechniquesReinforcement Learning in Robotics

Cited by 5,366

Explanation in Artificial Intelligence: Insights from the Social Sciences

Miller, T. (2017) · arXiv

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

Cited by 5,338

Dimensionality Reduction by Learning an Invariant Mapping

Hadsell, R., Chopra, S., & LeCun, Y. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06)

Face and Expression RecognitionNeural Networks and Applications

Cited by 5,336

ADADELTA: An Adaptive Learning Rate Method

Zeiler, M. D. (2012) · arXiv

Stochastic Gradient Optimization TechniquesMachine Learning and Data Classification

Cited by 5,322

Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries

Elad, M., & Aharon, M. (2006) · IEEE Transactions on Image Processing

Image and Signal Denoising MethodsImage Enhancement Techniques

Cited by 5,321

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al. (2016) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 5,319

DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks

Moosavi-Dezfooli, S.-M., Fawzi, A., & Frossard, P. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Adversarial Robustness in Machine LearningDigital Media Forensic Detection

Cited by 5,317

Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression

Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., & Ren, D. (2019) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 5,313

The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets

Saito, T., & Rehmsmeier, M. (2015) · PLOS ONE

Imbalanced Data Classification TechniquesMachine Learning and Data Classification

Cited by 5,309