Check computer science papers: citations, claims and retractions

HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge

Wang, H., Liu, C., Xi, N., Qiang, Z., Zhao, S., Qin, B., & Liu, T. (2023) · arXiv

Topic ModelingArtificial Intelligence in Healthcare and Education

Cited by 115

Seeing and identifying with a virtual body decreases pain perception

Hänsell, A., Lenggenhagerl, B., von Känell, R., Curatolol, M., & Blankel, O. (2011) · European Journal of Pain

Virtual Reality Applications and ImpactsPain Management and Treatment

Cited by 115

Reasoning about Pragmatics with Neural Listeners and Speakers

Andreas, J., & Klein, D. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

Multimodal Machine Learning ApplicationsSpeech and dialogue systems

Cited by 115

A Survey on Differentially Private Machine Learning [Review Article]

Gong, M., Xie, Y., Pan, K., Feng, K., & Qin, A. K. (2020) · IEEE Computational Intelligence Magazine

Privacy-Preserving Technologies in DataCloud Data Security Solutions

Cited by 115

Typing on flat glass: examining ten-finger expert typing patterns on touch surfaces

Findlater, L., Wobbrock, J. O., & Wigdor, D. (2011) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Interactive and Immersive DisplaysTactile and Sensory Interactions

Cited by 115

Towards automated semantic segmentation in prenatal volumetric ultrasound

Yang, X., Yu, L., Li, S., Wen, H., Luo, D., Bian, C., Qin, J., Ni, D., & Heng, P.-A. (2019) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 115

Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learning

Nguyen, K., & Daumé III, H. (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)

Multimodal Machine Learning ApplicationsSpeech and dialogue systems

Cited by 115

Calculating the thermal rate constant with exponential speedup on a quantum computer

Lidar, D. A., & Wang, H. (1999) · Physical Review E

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 115

Cohesion Metrics for Predicting Maintainability of Service-Oriented Software

Perepletchikov, M., Ryan, C., & Frampton, K. (2007) · Seventh International Conference on Quality Software (QSIC 2007)

Software Engineering ResearchAdvanced Software Engineering Methodologies

Cited by 115

Fusion of Facial Expressions and EEG for Multimodal Emotion Recognition

Huang, Y., Yang, J., Liao, P., & Pan, J. (2017) · Computational Intelligence and Neuroscience

Emotion and Mood RecognitionEEG and Brain-Computer Interfaces

Cited by 115

A General Framework for Counterfactual Learning-to-Rank

Agarwal, A., Takatsu, K., Zaitsev, I., & Joachims, T. (2019) · Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

Information Retrieval and Search BehaviorRecommender Systems and Techniques

Cited by 115

Robust localization using 3D NDT scan matching with experimentally determined uncertainty and road marker matching

Akai, N., Morales, L. Y., Takeuchi, E., Yoshihara, Y., & Ninomiya, Y. (2017) · 2017 IEEE Intelligent Vehicles Symposium (IV)

Robotics and Sensor-Based LocalizationAutonomous Vehicle Technology and Safety

Cited by 115

Towards Proving the Adversarial Robustness of Deep Neural Networks

Katz, G., Barrett, C., Dill, D. L., Julian, K., & Kochenderfer, M. J. (2017) · Electronic Proceedings in Theoretical Computer Science

Adversarial Robustness in Machine LearningFormal Methods in Verification

Cited by 115

MgSvF: Multi-Grained Slow versus Fast Framework for Few-Shot Class-Incremental Learning

Zhao, H., Fu, Y., Kang, M., Tian, Q., Wu, F., & Li, X. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 115

Chatgpt: a meta-analysis after 2.5 months

Leiter, C., Zhang, R., Chen, Y., Belouadi, J., Larionov, D., Fresen, V., & Eger, S. (2024) · Machine Learning with Applications

Sentiment Analysis and Opinion MiningEthics and Social Impacts of AI

Cited by 115

Wearable Sensor-Based Rehabilitation Exercise Assessment for Knee Osteoarthritis

Chen, K.-H., Chen, P.-C., Liu, K.-C., & Chan, C.-T. (2015) · Sensors

Context-Aware Activity Recognition SystemsGait Recognition and Analysis

Cited by 115

Efficient Precision-Adjustable Architecture for Softmax Function in Deep Learning

Zhu, D., Lu, S., Wang, M., Lin, J., & Wang, Z. (2020) · IEEE Transactions on Circuits and Systems II: Express Briefs

Numerical Methods and AlgorithmsAdvanced Neural Network Applications

Cited by 115

Token Merging for Fast Stable Diffusion

Bolya, D., & Hoffman, J. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Generative Adversarial Networks and Image SynthesisAdvanced Neural Network Applications

Cited by 115

An investigation of skill requirements in artificial intelligence and machine learning job advertisements

Verma, A., Lamsal, K., & Verma, P. (2022) · Industry and Higher Education

Information Systems Education and Curriculum DevelopmentHigher Education and Employability

Cited by 115

Self-Supervised Feature Learning by Learning to Spot Artifacts

Jenni, S., & Favaro, P. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Domain Adaptation and Few-Shot LearningGenerative Adversarial Networks and Image Synthesis

Cited by 115

GUIDEPOST: Creativity, Artificial Intelligence, and a World of Surprises Guidepost Letter for Academy of Management Discoveries

Amabile, T. (2019) · Academy of Management Discoveries

Artificial Intelligence ApplicationsCreativity in Education and Neuroscience

Cited by 115

Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detection

Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., & Kot, A. C. (2024) · IEEE Transactions on Information Forensics and Security

Digital Media Forensic DetectionBiometric Identification and Security

Cited by 115

Perturbation Sensitivity Analysis to Detect Unintended Model Biases

Prabhakaran, V., Hutchinson, B., & Mitchell, M. (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)

Topic ModelingNatural Language Processing Techniques

Cited by 115

Capturing Value from Artificial Intelligence

Berg, J. M., Raj, M., & Seamans, R. (2023) · Academy of Management Discoveries

Artificial Intelligence ApplicationsImpact of AI and Big Data on Business and Society

Cited by 115