Check computer science papers: citations, claims and retractions

TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation

Bao, K., Zhang, J., Zhang, Y., Wang, W., Feng, F., & He, X. (2023) · Proceedings of the 17th ACM Conference on Recommender Systems

Recommender Systems and TechniquesTopic Modeling

Cited by 469

Multi-layer Representation Learning for Medical Concepts

Choi, E., Bahadori, M. T., Searles, E., Coffey, C., Thompson, M., Bost, J., Tejedor-Sojo, J., & Sun, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Machine Learning in HealthcareMedical Coding and Health Information

Cited by 469

MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Zhu, D., Chen, J., Shen, X., Li, X., & Elhoseiny, M. (2023) · arXiv

Multimodal Machine Learning ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 469

The Out-of-Body Experience: Disturbed Self-Processing at the Temporo-Parietal Junction

Blanke, O., & Arzy, S. (2005) · The Neuroscientist

Virtual Reality Applications and ImpactsFree Will and Agency

Cited by 469

Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness

Kearns, M., Neel, S., Roth, A., & Wu, Z. S. (2017) · arXiv

Ethics and Social Impacts of AIPrivacy-Preserving Technologies in Data

Cited by 469

Design and perceptual validation of performance measures for salient object segmentation

Movahedi, V., & Elder, J. H. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops

Visual Attention and Saliency DetectionMedical Image Segmentation Techniques

Cited by 469

Human–AI Interactions in Public Sector Decision Making: “Automation Bias” and “Selective Adherence” to Algorithmic Advice

Alon-Barkat, S., & Busuioc, M. (2023) · Journal of Public Administration Research and Theory

Ethics and Social Impacts of AIE-Government and Public Services

Cited by 469

Ethical framework for Artificial Intelligence and Digital technologies

Ashok, M., Madan, R., Joha, A., & Sivarajah, U. (2022) · International Journal of Information Management

Ethics and Social Impacts of AIDigitalization, Law, and Regulation

Cited by 469

Many Labs 3: Evaluating participant pool quality across the academic semester via replication

Ebersole, C. R., Atherton, O. E., Belanger, A. L., Skulborstad, H. M., Allen, J. M., Banks, J. B., Baranski, E., Bernstein, M. J., Bonfiglio, D. B. V., Boucher, L., et al. (2016) · Journal of Experimental Social Psychology

Mobile Crowdsensing and CrowdsourcingPsychology Research and Bibliometrics

Cited by 468

FEELVOS: Fast End-To-End Embedding Learning for Video Object Segmentation

Voigtlaender, P., Chai, Y., Schroff, F., Adam, H., Leibe, B., & Chen, L.-C. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 468

Attend and Diagnose: Clinical Time Series Analysis Using Attention Models

Song, H., Rajan, D., Thiagarajan, J., & Spanias, A. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence

Machine Learning in HealthcareTime Series Analysis and Forecasting

Cited by 468

Blockchain technology in healthcare: Challenges and opportunities

Attaran, M. (2022) · International Journal of Healthcare Management

Blockchain Technology Applications and SecurityBlockchain Technology in Education and Learning

Cited by 468

SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods

Ribeiro, F. N., Araújo, M., Gonçalves, P., André Gonçalves, M., & Benevenuto, F. (2016) · EPJ Data Science

Sentiment Analysis and Opinion MiningHate Speech and Cyberbullying Detection

Cited by 468

RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans

Jin, Q., Meng, Z., Sun, C., Cui, H., & Su, R. (2020) · Frontiers in Bioengineering and Biotechnology

Medical Image Segmentation TechniquesAI in cancer detection

Cited by 468

Medical 4.0 technologies for healthcare: Features, capabilities, and applications

Haleem, A., Javaid, M., Pratap Singh, R., & Suman, R. (2022) · Internet of Things and Cyber-Physical Systems

Internet of Things and AITechnology and Human Factors in Education and Health

Cited by 468

Automatic Recognition of Facial Actions in Spontaneous Expressions

Bartlett, M. S., Littlewort, G. C., Frank, M. G., Lainscsek, C., Fasel, I. R., & Movellan, J. R. (2006) · Journal of Multimedia

Emotion and Mood RecognitionFace recognition and analysis

Cited by 468

Building Classifiers with Independency Constraints

Calders, T., Kamiran, F., & Pechenizkiy, M. (2009) · 2009 IEEE International Conference on Data Mining Workshops

Machine Learning and Data ClassificationMachine Learning and Algorithms

Cited by 468

OpenID 2.0: a platform for user-centric identity management

Recordon, D., & Reed, D. (2006) · Proceedings of the second ACM workshop on Digital identity management

User Authentication and Security SystemsAdvanced Authentication Protocols Security

Cited by 467

Long-Term Trends in the Public Perception of Artificial Intelligence

Fast, E., & Horvitz, E. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence

Ethics and Social Impacts of AISentiment Analysis and Opinion Mining

Cited by 467

Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks

Horie, Y., Yoshio, T., Aoyama, K., Yoshimizu, S., Horiuchi, Y., Ishiyama, A., Hirasawa, T., Tsuchida, T., Ozawa, T., Ishihara, S., et al. (2019) · Gastrointestinal Endoscopy

AI in cancer detectionEsophageal Cancer Research and Treatment

Cited by 467

Model-Based Reinforcement Learning for Atari

Kaiser, L., Babaeizadeh, M., Milos, P., Osinski, B., Campbell, R. H., Czechowski, K., Erhan, D., Finn, C., Kozakowski, P., Levine, S., et al. (2019) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 467

Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning

Wang, X., Liu, N., Han, H., & Shi, C. (2021) · Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

Advanced Graph Neural NetworksRecommender Systems and Techniques

Cited by 467

An overview of SuperLU: Algorithms, implementation, and user interface

Li, X. S. (2005) · ACM Transactions on Mathematical Software

Matrix Theory and AlgorithmsParallel Computing and Optimization Techniques

Cited by 467

Chameleon: scalable adaptation of video analytics

Jiang, J., Ananthanarayanan, G., Bodik, P., Sen, S., & Stoica, I. (2018) · Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication

Advanced Neural Network ApplicationsVideo Surveillance and Tracking Methods

Cited by 467