Check computer science papers: citations, claims and retractions

Does Writing with Language Models Reduce Content Diversity?

Padmakumar, V., & He, H. (2023) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 21

PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts

Bach, S. H., Sanh, V., Yong, Z.-X., Webson, A., Raffel, C., Nayak, N. V., Sharma, A., Kim, T., Bari, M. S., Fevry, T., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 21

Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study

Wang, B., Ping, W., Xu, P., McAfee, L., Liu, Z., Shoeybi, M., Dong, Y., Kuchaiev, O., Li, B., Xiao, C., et al. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingInformation Retrieval and Search Behavior

Cited by 21

A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks

Wang, J., Jiang, H., Liu, Y., Ma, C., Zhang, X., Pan, Y., Liu, M., Gu, P., Xia, S., Chen, L., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 21

Integrating plan-based behavior generation with game environments

Young, R. M., & Riedl, M. O. (2005) · Proceedings of the 2005 ACM SIGCHI International Conference on Advances in computer entertainment technology

Artificial Intelligence in GamesAI-based Problem Solving and Planning

Cited by 21

Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents

Shi, Z., Gao, S., Yan, L., Feng, Y., Chen, X., Chen, Z., Yin, D., Verberne, S., & Ren, Z. (2025) · Proceedings of the ACM on Web Conference 2025

Topic ModelingNatural Language Processing Techniques

Cited by 21

Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agents

Sharma, N., Zhang, Z., Lee, D., Krishnan, N., Ren, G.-J., Xiao, Z., & Li, Y. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems

Speech and dialogue systemsAI in Service Interactions

Cited by 21

Design and Evaluation of Hybrid Search for American Sign Language to English Dictionaries: Making the Most of Imperfect Sign Recognition

Hassan, S., Amin, A. A., Gordon, A., Lee, S., & Huenerfauth, M. (2022) · CHI Conference on Human Factors in Computing Systems

Usability and User Interface DesignInformation Retrieval and Search Behavior

Cited by 21

Identification of Rare Wildlife in the Field Environment Based on the Improved YOLOv5 Model

Su, X., Zhang, J., Ma, Z., Dong, Y., Zi, J., Xu, N., Zhang, H., Xu, F., & Chen, F. (2024) · Remote Sensing

Advanced Neural Network ApplicationsSmart Agriculture and AI

Cited by 21

Interpretable Directed Diversity: Leveraging Model Explanations for Iterative Crowd Ideation

Wang, Y., Venkatesh, P., & Lim, B. Y. (2022) · CHI Conference on Human Factors in Computing Systems

Explainable Artificial Intelligence (XAI)Design Education and Practice

Cited by 21

From bard to Gemini: An investigative exploration journey through Google’s evolution in conversational AI and generative AI

Bin Akhtar, Z. (2024) · Computing and artificial intelligence.

Artificial Intelligence ApplicationsAI in Service Interactions

Cited by 21

Measuring AI Ability to Complete Long Software Tasks

Kwa, T., West, B., Becker, J., Deng, A., Garcia, K., Hasin, M., Jawhar, S., Kinniment, M., Rush, N., Von Arx, S., et al. (2025) · Advances in Neural Information Processing Systems 38

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 21

Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps

Dao, T., Sohoni, N. S., Gu, A., Eichhorn, M., Blonder, A., Leszczynski, M., Rudra, A., & Ré, C. (2020) · arXiv

Advanced Neural Network ApplicationsNeural Networks and Applications

Cited by 21

Combining texture features of whole slide images improves prognostic prediction of recurrence-free survival for cutaneous melanoma patients

Peng, Y., Chu, Y., Chen, Z., Zhou, W., Wan, S., Xiao, Y., Zhang, Y., & Li, J. (2020) · World Journal of Surgical Oncology

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 21

Post Classification Label Refinement Using Implicit Ordering Constraint Among Data Instances

Khandelwal, A., Mithal, V., & Kumar, V. (2015) · 2015 IEEE International Conference on Data Mining

Machine Learning and Data ClassificationRemote-Sensing Image Classification

Cited by 21

Toward Universal Text-To-Music Retrieval

Doh, S., Won, M., Choi, K., & Nam, J. (2023) · ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Music and Audio ProcessingRecommender Systems and Techniques

Cited by 21

Opportunities and challenges for biomarker discovery using electronic health record data

Singhal, P., Tan, A. L. M., Drivas, T. G., Johnson, K. B., Ritchie, M. D., & Beaulieu-Jones, B. K. (2023) · Trends in Molecular Medicine

Machine Learning in HealthcareElectronic Health Records Systems

Cited by 21

Accessible to Whom? Bringing Accessibility to Blocks

Stefik, A., Allee, W., Contreras, G., Kluthe, T., Hoffman, A., Blaser, B., & Ladner, R. (2024) · Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1

Teaching and Learning ProgrammingDigital Accessibility for Disabilities

Cited by 21

Module Lattice-Based Post Quantum Secure Blockchain Empowered Authentication Framework for Autonomous Truck Platooning

Chaudhary, D., Santhi, P., Durgarao, M. S. P., Padmavathi, A., Mehedi Hassan, M., & Fahad Alkhamees, B. (2024) · IEEE Access

Cryptography and Data SecurityAdvanced Authentication Protocols Security

Cited by 21

Registration of 2D x-ray images to 3D MRI by generating pseudo-CT data

van der Bom, M. J., Pluim, J. P. W., Gounis, M. J., van de Kraats, E. B., Sprinkhuizen, S. M., Timmer, J., Homan, R., & Bartels, L. W. (2011) · Physics in Medicine and Biology

Medical Image Segmentation TechniquesAdvanced Radiotherapy Techniques

Cited by 21

Otago Exercises Monitoring for Older Adults by a Single IMU and Hierarchical Machine Learning Models

Shang, M., Dedeyne, L., Dupont, J., Vercauteren, L., Amini, N., Lapauw, L., Gielen, E., Verschueren, S., Varon, C., De Raedt, W., et al. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering

Context-Aware Activity Recognition SystemsPhysical Activity and Health

Cited by 21

Internet of Things for Pervasive and Personalized Healthcare: Architecture, Technologies, Components, Applications, and Prototype Development

Kolli, S., V., P. K. A., Ashok, J., & Manikandan, A. (2023) · Advances in Healthcare Information Systems and Administration

Internet of Things and AIIoT and Edge/Fog Computing

Cited by 21

Adaptive Circuit Learning for Quantum Metrology

Ma, Z., Gokhale, P., Zheng, T.-X., Zhou, S., Yu, X., Jiang, L., Maurer, P., & Chong, F. T. (2020) · arXiv

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 21

Large Language Models can be Guided to Evade AI-Generated Text Detection

Lu, N., Liu, S., He, R., Wang, Q., Ong, Y.-S., & Tang, K. (2023) · arXiv

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 21