Check computer science papers: citations, claims and retractions

State of the art survey on MRI brain tumor segmentation

Gordillo, N., Montseny, E., & Sobrevilla, P. (2013) · Magnetic Resonance Imaging

Brain Tumor Detection and ClassificationMedical Image Segmentation Techniques

Cited by 697

Challenges in representation learning: A report on three machine learning contests

Goodfellow, I. J., Erhan, D., Luc Carrier, P., Courville, A., Mirza, M., Hamner, B., Cukierski, W., Tang, Y., Thaler, D., Lee, D.-H., et al. (2015) · Neural Networks

Domain Adaptation and Few-Shot LearningMachine Learning and Data Classification

Cited by 697

NRC-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews

Kiritchenko, S., Zhu, X., Cherry, C., & Mohammad, S. (2014) · Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

Sentiment Analysis and Opinion MiningTopic Modeling

Cited by 697

Regularizing and Optimizing LSTM Language Models

Merity, S., Keskar, N. S., & Socher, R. (2017) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 697

A Unified MRC Framework for Named Entity Recognition

Li, X., Feng, J., Meng, Y., Han, Q., Wu, F., & Li, J. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 697

Time-Series Representation Learning via Temporal and Contextual Contrasting

Eldele, E., Ragab, M., Chen, Z., Wu, M., Kwoh, C. K., Li, X., & Guan, C. (2021) · Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence

Time Series Analysis and ForecastingDomain Adaptation and Few-Shot Learning

Cited by 696

TuckER: Tensor Factorization for Knowledge Graph Completion

Balazevic, I., Allen, C., & Hospedales, T. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Advanced Graph Neural NetworksTopic Modeling

Cited by 696

Learning to Ask: Neural Question Generation for Reading Comprehension

Du, X., Shao, J., & Cardie, C. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 696

Detecting and classifying lesions in mammograms with Deep Learning

Ribli, D., Horváth, A., Unger, Z., Pollner, P., & Csabai, I. (2018) · Scientific Reports

AI in cancer detectionDigital Radiography and Breast Imaging

Cited by 696

Rethinking Attention with Performers

Choromanski, K., Likhosherstov, V., Dohan, D., Song, X., Gane, A., Sarlos, T., Hawkins, P., Davis, J., Mohiuddin, A., Kaiser, L., et al. (2020) · arXiv

Topic ModelingAdvanced Neural Network Applications

Cited by 695

Illusory ownership of a virtual child body causes overestimation of object sizes and implicit attitude changes

Banakou, D., Groten, R., & Slater, M. (2013) · Proceedings of the National Academy of Sciences

Virtual Reality Applications and ImpactsMultisensory perception and integration

Cited by 695

COVAREP — A collaborative voice analysis repository for speech technologies

Degottex, G., Kane, J., Drugman, T., Raitio, T., & Scherer, S. (2014) · 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Emotion and Mood RecognitionSpeech Recognition and Synthesis

Cited by 695

Learning to Drive in a Day

Kendall, A., Hawke, J., Janz, D., Mazur, P., Reda, D., Allen, J.-M., Lam, V.-D., Bewley, A., & Shah, A. (2019) · 2019 International Conference on Robotics and Automation (ICRA)

Reinforcement Learning in RoboticsAutonomous Vehicle Technology and Safety

Cited by 694

Mobile Phone Based Clinical Microscopy for Global Health Applications

Breslauer, D. N., Maamari, R. N., Switz, N. A., Lam, W. A., & Fletcher, D. A. (2009) · PLoS ONE

Digital Imaging for Blood DiseasesBiosensors and Analytical Detection

Cited by 694

BMN: Boundary-Matching Network for Temporal Action Proposal Generation

Lin, T., Liu, X., Li, X., Ding, E., & Wen, S. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Human Pose and Action RecognitionMultimodal Machine Learning Applications

Cited by 694

An era of ChatGPT as a significant futuristic support tool: A study on features, abilities, and challenges

Haleem, A., Javaid, M., & Singh, R. P. (2022) · BenchCouncil Transactions on Benchmarks, Standards and Evaluations

Artificial Intelligence ApplicationsTopic Modeling

Cited by 694

Evidential Deep Learning to Quantify Classification Uncertainty

Sensoy, M., Kaplan, L., & Kandemir, M. (2018) · arXiv

Adversarial Robustness in Machine LearningAdvanced Neural Network Applications

Cited by 694

Generating Radiology Reports via Memory-driven Transformer

Chen, Z., Song, Y., Chang, T.-H., & Wan, X. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 693

Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers

Gao, J., Lanchantin, J., Soffa, M. L., & Qi, Y. (2018) · 2018 IEEE Security and Privacy Workshops (SPW)

Adversarial Robustness in Machine LearningSoftware Testing and Debugging Techniques

Cited by 693

A visual–language foundation model for pathology image analysis using medical twitter

Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J., & Zou, J. (2023) · Nature Medicine

Multimodal Machine Learning ApplicationsAI in cancer detection

Cited by 693

MFNet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes

Ha, Q., Watanabe, K., Karasawa, T., Ushiku, Y., & Harada, T. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Advanced Neural Network ApplicationsAutonomous Vehicle Technology and Safety

Cited by 693

Interoperability in Internet of Things: Taxonomies and Open Challenges

Noura, M., Atiquzzaman, M., & Gaedke, M. (2019) · Mobile Networks and Applications

IoT and Edge/Fog ComputingInternet of Things and AI

Cited by 693

Augmenting the eye of the beholder: exploring the strategic potential of augmented reality to enhance online service experiences

Hilken, T., de Ruyter, K., Chylinski, M., Mahr, D., & Keeling, D. I. (2017) · Journal of the Academy of Marketing Science

Augmented Reality ApplicationsAI in Service Interactions

Cited by 693

RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models

Gehman, S., Gururangan, S., Sap, M., Choi, Y., & Smith, N. A. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020

Hate Speech and Cyberbullying DetectionAdversarial Robustness in Machine Learning

Cited by 693