Check computer science papers: citations, claims and retractions

Non-Autoregressive Line-Level Code Completion

Liu, F., Fu, Z., Li, G., Jin, Z., Liu, H., Hao, Y., & Zhang, L. (2024) · ACM Transactions on Software Engineering and Methodology

Software Engineering ResearchSoftware Testing and Debugging Techniques

Cited by 22

From Today's Code to Tomorrow's Symphony: The AI Transformation of Developer's Routine by 2030

Qiu, K., Puccinelli, N., Ciniselli, M., & Di Grazia, L. (2024) · arXiv

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 22

Carbon-Aware, Energy-Efficient, and SLA-Compliant Virtual Machine Placement in Cloud Data Centers Using Deep Q-Networks and Agglomerative Clustering

Alex, M., Ojo, S. O., & Awuor, F. M. (2025) · Computers

Cloud Computing and Resource ManagementEnergy Efficiency in Computing

Cited by 22

Distributed Online Learning of Fog Computing Under Nonuniform Device Cardinality

Ren, C., Lyu, X., Ni, W., Tian, H., & Liu, R. P. (2019) · IEEE Internet of Things Journal

IoT and Edge/Fog ComputingEnergy Efficiency in Computing

Cited by 22

Structured Model Pruning of Convolutional Networks on Tensor Processing Units

Chen, K., Franko, K., & Sang, R. (2021) · arXiv

Advanced Neural Network ApplicationsMachine Learning and Data Classification

Cited by 22

Evaluating the World Model Implicit in a Generative Model

Vafa, K., Chen, J., Rambachan, A., Kleinberg, J., & Mullainathan, S. (2024) · Advances in Neural Information Processing Systems 37

Reinforcement Learning in RoboticsBayesian Modeling and Causal Inference

Cited by 22

Prompt-Eng: Healthcare Prompt Engineering: Revolutionizing Healthcare Applications with Precision Prompts

Ahmed, A., Hou, M., Xi, R., Zeng, X., & Shah, S. A. (2024) · Companion Proceedings of the ACM Web Conference 2024

Topic ModelingMachine Learning in Healthcare

Cited by 22

Style-Aware Radiology Report Generation with RadGraph and Few-Shot Prompting

Yan, B., Liu, R., Kuo, D., Adithan, S., Reis, E., Kwak, S., Venugopal, V., O’Connell, C., Saenz, A., Rajpurkar, P., et al. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 22

SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Tschannen, M., Gritsenko, A., Wang, X., Naeem, M. F., Alabdulmohsin, I., Parthasarathy, N., Evans, T., Beyer, L., Xia, Y., Mustafa, B., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 22

Recent Advances in Generative Information Retrieval

Tang, Y., Zhang, R., Guo, J., & de Rijke, M. (2023) · Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region

Information Retrieval and Search BehaviorTopic Modeling

Cited by 22

A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communication

Luccioni, A. S., Corry, F., Sridharan, H., Ananny, M., Schultz, J., & Crawford, K. (2021) · arXiv

Ethics and Social Impacts of AIResearch Data Management Practices

Cited by 22

Online learning for the hyoid bone tracking during swallowing with neck movement adjustment using semantic segmentation

Lee, D., Lee, W. H., Seo, H. G., Oh, B.-M., Lee, J. C., & Kim, H. C. (2020) · IEEE Access

Medical Image Segmentation TechniquesDysphagia Assessment and Management

Cited by 22

Large scale multi-actor generative dialog modeling,

Boyd, A., Puri, R., Shoeybi, M., Patwary, M., & Catanzaro, B. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Speech and dialogue systemsTopic Modeling

Cited by 22

Can Large Language Models be Trusted for Evaluation? Scalable Meta-Evaluation of LLMs as Evaluators via Agent Debate

Chern, S., Chern, E., Neubig, G., & Liu, P. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 22

Dealing with complexity: How to use a hybrid approach to incorporate complexity in health behavior interventions

Mkhitaryan, S., Giabbanelli, P. J., de Vries, N. K., & Crutzen, R. (2020) · Intelligence-Based Medicine

Cognitive Science and MappingBehavioral Health and Interventions

Cited by 22

Prompting GPT-3.5 for Text-to-SQL with De-semanticization and Skeleton Retrieval

Guo, C., Tian, Z., Tang, J., Wang, P., Wen, Z., Yang, K., & Wang, T. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 22

Open Source Software Sustainability: Combining Institutional Analysis and Socio-Technical Networks

Yin, L., Chakraborti, M., Yan, Y., Schweik, C., Frey, S., & Filkov, V. (2022) · Proceedings of the ACM on Human-Computer Interaction

Open Source Software InnovationsSoftware Engineering Techniques and Practices

Cited by 22

An Ensemble Approach for Extractive Text Summarization

Singh, P., Chhikara, P., & Singh, J. (2020) · 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE)

Advanced Text Analysis TechniquesTopic Modeling

Cited by 22

A taxonomy for attacks on mobile agent

Man, M. C., & Wei, V. K. (2002) · EUROCON'2001. International Conference on Trends in Communications. Technical Program, Proceedings (Cat. No.01EX439)

Mobile Agent-Based Network ManagementSecurity and Verification in Computing

Cited by 22

"Taking Care of a Fruit Tree": Nurturing as a Layer of Concern in Online Community Moderation

Yu, B., Seering, J., Spiel, K., & Watts, L. (2020) · Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems

Innovative Human-Technology InteractionHate Speech and Cyberbullying Detection

Cited by 22

Speech-Based Emotion Recognition with Self-Supervised Models Using Attentive Channel-Wise Correlations and Label Smoothing

Kakouros, S., Stafylakis, T., Mošner, L., & Burget, L. (2023) · ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Emotion and Mood RecognitionSpeech Recognition and Synthesis

Cited by 22

Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation

Wang, W., Levine, A., & Feizi, S. (2022) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 22

Multi-Source Neural Variational Inference

Kurle, R., Günnemann, S., & Van der Smagt, P. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence

Gaussian Processes and Bayesian InferenceTopic Modeling

Cited by 22

Routerdc: Query-based router by dual contrastive learning for assembling large language models

Chen, S., Jiang, W., Lin, B., Kwok, J., & Zhang, Y. (2024) · Advances in Neural Information Processing Systems 37

Topic ModelingInformation Retrieval and Search Behavior

Cited by 22