Check computer science papers: citations, claims and retractions

MTEB: Massive Text Embedding Benchmark

Muennighoff, N., Tazi, N., Magne, L., & Reimers, N. (2023) · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Topic ModelingMultimodal Machine Learning Applications

Cited by 548

Personalized adaptive learning: an emerging pedagogical approach enabled by a smart learning environment

Peng, H., Ma, S., & Spector, J. M. (2019) · Smart Learning Environments

Intelligent Tutoring Systems and Adaptive LearningEducation and Learning Interventions

Cited by 548

Towards a standard for identifying and managing bias in artificial intelligence

Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A., & Hall, P. (2022)

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 548

Drone-Based Object Counting by Spatially Regularized Regional Proposal Network

Hsieh, M.-R., Lin, Y.-L., & Hsu, W. H. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsVideo Surveillance and Tracking Methods

Cited by 547

Demonstration of the trapped-ion quantum CCD computer architecture

Pino, J. M., Dreiling, J. M., Figgatt, C., Gaebler, J. P., Moses, S. A., Allman, M. S., Baldwin, C. H., Foss-Feig, M., Hayes, D., Mayer, K., et al. (2021) · Nature

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 547

How We Refactor, and How We Know It

Murphy-Hill, E., Parnin, C., & Black, A. P. (2012) · IEEE Transactions on Software Engineering

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 547

DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning

Graving, J. M., Chae, D., Naik, H., Li, L., Koger, B., Costelloe, B. R., & Couzin, I. D. (2019) · eLife

Human Pose and Action RecognitionAdvanced Neural Network Applications

Cited by 547

Intelligible models for classification and regression

Lou, Y., Caruana, R., & Gehrke, J. (2012) · Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

Explainable Artificial Intelligence (XAI)Statistical Methods and Applications

Cited by 547

DARPA's explainable artificial intelligence (XAI) program

Gunning, D. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces

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

Cited by 546

OpenSlide: A vendor-neutral software foundation for digital pathology

Goode, A., Gilbert, B., Harkes, J., Jukic, D., & Satyanarayanan, M. (2013) · Journal of Pathology Informatics

AI in cancer detectionArtificial Intelligence in Healthcare and Education

Cited by 546

Rtfnet: Rgb-thermal fusion network for semantic segmentation of urban scenes

Sun, Y., Zuo, W., & Liu, M. (2019) · IEEE Robotics and Automation Letters

Advanced Neural Network ApplicationsAdvanced Image Fusion Techniques

Cited by 546

Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI

Jacovi, A., Marasović, A., Miller, T., & Goldberg, Y. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

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

Cited by 545

Deep hashing for compact binary codes learning

Liong, V. E., Lu, J., Wang, G., Moulin, P., & Zhou, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Image and Video Retrieval TechniquesImage Retrieval and Classification Techniques

Cited by 545

Ancestral graph Markov models

Richardson, T., & Spirtes, P. (2002) · The Annals of Statistics

Bayesian Modeling and Causal InferenceStatistical Methods and Inference

Cited by 545

YOLACT++ Better Real-Time Instance Segmentation

Bolya, D., Zhou, C., Xiao, F., & Lee, Y. J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 544

Deep learning-based classification of mesothelioma improves prediction of patient outcome

Courtiol, P., Maussion, C., Moarii, M., Pronier, E., Pilcer, S., Sefta, M., Manceron, P., Toldo, S., Zaslavskiy, M., Le Stang, N., et al. (2019) · Nature Medicine

AI in cancer detectionOccupational and environmental lung diseases

Cited by 544

Artificial intelligence and computational pathology

Cui, M., & Zhang, D. Y. (2021) · Laboratory Investigation

AI in cancer detectionArtificial Intelligence in Healthcare and Education

Cited by 544

Artificial Intelligence and Digital Pathology: Challenges and Opportunities

Tizhoosh, H. R., & Pantanowitz, L. (2018) · Journal of Pathology Informatics

AI in cancer detectionArtificial Intelligence in Healthcare and Education

Cited by 544

Mathematical discoveries from program search with large language models

Romera-Paredes, B., Barekatain, M., Novikov, A., Balog, M., Kumar, M. P., Dupont, E., Ruiz, F. J. R., Ellenberg, J. S., Wang, P., Fawzi, O., et al. (2024) · Nature

Evolutionary Algorithms and ApplicationsArtificial Intelligence Applications

Cited by 544

Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

Zhang, Y., Li, Y., Cui, L., Cai, D., Liu, L., Fu, T., Huang, X., Zhao, E., Zhang, Y., Xu, C., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 544

An introduction to deep reinforcement learning

François-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., & Pineau, J. (2018) · now publishers, Inc. eBooks

Reinforcement Learning in RoboticsAdvanced Bandit Algorithms Research

Cited by 544

The poor availability of psychological research data for reanalysis.

Wicherts, J. M., Borsboom, D., Kats, J., & Molenaar, D. (2006) · American Psychologist

Research Data Management PracticesData Analysis and Archiving

Cited by 543

Shrinkage Fields for Effective Image Restoration

Schmidt, U., & Roth, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition

Advanced Image Processing TechniquesImage and Signal Denoising Methods

Cited by 543

Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network

Sharma, H., Park, J., Suda, N., Lai, L., Chau, B., Kim, J. K., Chandra, V., & Esmaeilzadeh, H. (2018) · 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA)

Advanced Neural Network ApplicationsParallel Computing and Optimization Techniques

Cited by 543