Check computer science papers: citations, claims and retractions

GPT Understands, Too

Liu, X., Zheng, Y., Du, Z., Ding, M., Qian, Y., Yang, Z., & Tang, J. (2021) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 908

Backpropagation and the brain

Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J., & Hinton, G. (2020) · Nature Reviews Neuroscience

Neural Networks and ApplicationsNeural dynamics and brain function

Cited by 908

Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling

Marcheggiani, D., & Titov, I. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Natural Language Processing TechniquesTopic Modeling

Cited by 907

Annotation Artifacts in Natural Language Inference Data

Gururangan, S., Swayamdipta, S., Levy, O., Schwartz, R., Bowman, S., & Smith, N. A. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Natural Language Processing TechniquesTopic Modeling

Cited by 907

Interactive machine learning for health informatics: when do we need the human-in-the-loop?

Holzinger, A. (2016) · Brain Informatics

Machine Learning and AlgorithmsArtificial Intelligence in Healthcare and Education

Cited by 906

Designing Theory-Driven User-Centric Explainable AI

Wang, D., Yang, Q., Abdul, A., & Lim, B. Y. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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

Cited by 906

Common Voice: A Massively-Multilingual Speech Corpus

Ardila, R., Branson, M., Davis, K., Henretty, M., Kohler, M., Meyer, J., Morais, R., Saunders, L., Tyers, F. M., & Weber, G. (2019) · arXiv

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 905

Plug-and-Play ADMM for Image Restoration: Fixed-Point Convergence and Applications

Chan, S. H., Wang, X., & Elgendy, O. A. (2017) · IEEE Transactions on Computational Imaging

Image and Signal Denoising MethodsAdvanced Image Processing Techniques

Cited by 904

Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network

Trigeorgis, G., Ringeval, F., Brueckner, R., Marchi, E., Nicolaou, M. A., Schuller, B., & Zafeiriou, S. (2016) · 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Emotion and Mood RecognitionSpeech Recognition and Synthesis

Cited by 904

Virtual reality exposure therapy for anxiety disorders: A meta-analysis

Powers, M. B., & Emmelkamp, P. M. G. (2008) · Journal of Anxiety Disorders

Virtual Reality Applications and ImpactsAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes

Cited by 904

CondenseNet: An Efficient DenseNet Using Learned Group Convolutions

Huang, G., Liu, S., Maaten, L. V. D., & Weinberger, K. Q. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 904

COMET: Commonsense Transformers for Automatic Knowledge Graph Construction

Bosselut, A., Rashkin, H., Sap, M., Malaviya, C., Celikyilmaz, A., & Choi, Y. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingMultimodal Machine Learning Applications

Cited by 904

Non-interactive zero-knowledge and its applications

Blum, M., Feldman, P., & Micali, S. (1988) · Proceedings of the twentieth annual ACM symposium on Theory of computing - STOC '88

Cryptography and Data SecurityAdvanced Authentication Protocols Security

Cited by 904

“Who are these people?” Evaluating the demographic characteristics and political preferences of MTurk survey respondents

Huff, C., & Tingley, D. (2015) · Research & Politics

Mobile Crowdsensing and CrowdsourcingSurvey Methodology and Nonresponse

Cited by 903

Transformer Interpretability Beyond Attention Visualization

Chefer, H., Gur, S., & Wolf, L. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Explainable Artificial Intelligence (XAI)Advanced Neural Network Applications

Cited by 903

Addressing bias in big data and AI for health care: A call for open science

Norori, N., Hu, Q., Aellen, F. M., Faraci, F. D., & Tzovara, A. (2021) · Patterns

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 903

BirdNET: A deep learning solution for avian diversity monitoring

Kahl, S., Wood, C. M., Eibl, M., & Klinck, H. (2021) · Ecological Informatics

Music and Audio ProcessingAnimal Vocal Communication and Behavior

Cited by 903

Deep learning in histopathology: the path to the clinic

van der Laak, J., Litjens, G., & Ciompi, F. (2021) · Nature Medicine

AI in cancer detectionArtificial Intelligence in Healthcare and Education

Cited by 902

An Analysis of Data Quality: Professional Panels, Student Subject Pools, and Amazon's Mechanical Turk

Kees, J., Berry, C., Burton, S., & Sheehan, K. (2017) · Journal of Advertising

Mobile Crowdsensing and CrowdsourcingSurvey Methodology and Nonresponse

Cited by 902

Distributed optimization in sensor networks

Rabbat, M., & Nowak, R. (2004) · Proceedings of the 3rd international symposium on Information processing in sensor networks

Distributed Sensor Networks and Detection AlgorithmsDistributed Control Multi-Agent Systems

Cited by 901

Beyond Accuracy: Behavioral Testing of NLP models with CheckList

Ribeiro, M. T., Wu, T., Guestrin, C., & Singh, S. (2020) · arXiv

Software Testing and Debugging TechniquesTopic Modeling

Cited by 901

Density‐based clustering

Kriegel, H., Kröger, P., Sander, J., & Zimek, A. (2011) · WIREs Data Mining and Knowledge Discovery

Advanced Clustering Algorithms ResearchData Management and Algorithms

Cited by 900

Question Asking During Tutoring

Graesser, A. C., & Person, N. K. (1994) · American Educational Research Journal

Intelligent Tutoring Systems and Adaptive LearningInnovative Teaching and Learning Methods

Cited by 900

Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods

Slack, D., Hilgard, S., Jia, E., Singh, S., & Lakkaraju, H. (2020) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

Explainable Artificial Intelligence (XAI)Adversarial Robustness in Machine Learning

Cited by 900