Check computer science papers: citations, claims and retractions

Universal Approximation Depth and Errors of Narrow Belief Networks with Discrete Units

Montúfar, G. F. (2014) · Neural Computation

Neural Networks and ApplicationsBayesian Modeling and Causal Inference

Cited by 38

Reinforcement Learning of Active Vision for Manipulating Objects under Occlusions

Cheng, R., Agarwal, A., & Fragkiadaki, K. (2018) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 38

Notable: On-the-fly Assistant for Data Storytelling in Computational Notebooks

Li, H., Ying, L., Zhang, H., Wu, Y., Qu, H., & Wang, Y. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

Data Visualization and AnalyticsScientific Computing and Data Management

Cited by 38

Combining Induction, Deduction, and Structure for Verification and Synthesis

Seshia, S. A. (2015) · Proceedings of the IEEE

Formal Methods in VerificationLogic, programming, and type systems

Cited by 38

Automatic analysis of medical dialogue in the home hemodialysis domain: Structure induction and summarization

Lacson, R. C., Barzilay, R., & Long, W. J. (2006) · Journal of Biomedical Informatics

Speech and dialogue systemsBiomedical Text Mining and Ontologies

Cited by 38

Network-Aware Virtual Machine Placement and Migration in Cloud Data Centers

Ferdaus, M. H., Murshed, M., Calheiros, R. N., & Buyya, R. (2015) · Advances in Systems Analysis, Software Engineering, and High Performance Computing

Cloud Computing and Resource ManagementEnergy Efficiency in Computing

Cited by 38

Evaluating Inclusivity using Quantitative Personas

Goodman-Deane, J., Waller, S., Demin, D., González-de-Heredia, A., Bradley, M., & Clarkson, J. P. (2018) · Proceedings of DRS

Persona Design and ApplicationsDigital Accessibility for Disabilities

Cited by 38

Deep Transformer Q-Networks for Partially Observable Reinforcement Learning

Esslinger, K., Platt, R., & Amato, C. (2022) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 38

Text Backdoor Detection Using an Interpretable RNN Abstract Model

Fan, M., Si, Z., Xie, X., Liu, Y., & Liu, T. (2021) · IEEE Transactions on Information Forensics and Security

Adversarial Robustness in Machine LearningExplainable Artificial Intelligence (XAI)

Cited by 38

The application of artificial intelligence to biology and neuroscience

Richards, B., Tsao, D., & Zador, A. (2022) · Cell

Artificial Intelligence ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 38

Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions

Roller, S., Boureau, Y.-L., Weston, J., Bordes, A., Dinan, E., Fan, A., Gunning, D., Ju, D., Li, M., Poff, S., et al. (2020) · arXiv

Speech and dialogue systemsAI in Service Interactions

Cited by 38

A Novel Lightweight IoT Intrusion Detection Model Based on Self-Knowledge Distillation

Wang, Z., Zhou, R., Yang, S., He, D., & Chan, S. (2025) · IEEE Internet of Things Journal

Network Security and Intrusion DetectionInternet Traffic Analysis and Secure E-voting

Cited by 38

Model-Agnostic Method for Thoracic Wall Segmentation in Fetal Ultrasound Videos

Shozu, K., Komatsu, M., Sakai, A., Komatsu, R., Dozen, A., Machino, H., Yasutomi, S., Arakaki, T., Asada, K., Kaneko, S., et al. (2020) · Biomolecules

Medical Image Segmentation TechniquesArtificial Intelligence in Healthcare and Education

Cited by 38

An analysis of three chatbots: BlenderBot, ChatGPT and LaMDA

O'Leary, D. E. (2023) · Intelligent Systems in Accounting, Finance and Management

Speech and dialogue systemsTopic Modeling

Cited by 38

Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey

Kumar, S., Balachandran, V., Njoo, L., Anastasopoulos, A., & Tsvetkov, Y. (2023) · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Ethics and Social Impacts of AIHate Speech and Cyberbullying Detection

Cited by 38

Machine Learning Operations: A Survey on MLOps Tool Support

Hewage, N., & Meedeniya, D. (2022) · arXiv

Software Engineering Techniques and PracticesSoftware System Performance and Reliability

Cited by 38

Quality of Experience-Centric Management of Adaptive Video Streaming Services: Status and Challenges

Petrangeli, S., Hooft, J. V. D., Wauters, T., & Turck, F. D. (2018) · ACM Transactions on Multimedia Computing, Communications, and Applications

Image and Video Quality AssessmentNetwork Traffic and Congestion Control

Cited by 38

LAFITE: Towards Language-Free Training for Text-to-Image Generation

Zhou, Y., Zhang, R., Chen, C., Li, C., Tensmeyer, C., Yu, T., Gu, J., Xu, J., & Sun, T. (2021) · arXiv

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 38

FPGA-Based Convolutional Neural Network Architecture with Reduced Parameter Requirements

Hailesellasie, M., Hasan, S. R., Khalid, F., Wad, F. A., & Shafique, M. (2018) · 2018 IEEE International Symposium on Circuits and Systems (ISCAS)

Advanced Neural Network ApplicationsEmbedded Systems Design Techniques

Cited by 38

The disciplinary power of predictive algorithms: a Foucauldian perspective

de Laat, P. B. (2019) · Ethics and Information Technology

Ethics and Social Impacts of AIFoucault, Power, and Ethics

Cited by 38

Analysis of the adoption of security headers in HTTP

Buchanan, W. J., Helme, S., & Woodward, A. (2018) · IET Information Security

Web Application Security VulnerabilitiesInformation and Cyber Security

Cited by 38

Explainable AI for time series via Virtual Inspection Layers

Vielhaben, J., Lapuschkin, S., Montavon, G., & Samek, W. (2024) · Pattern Recognition

Explainable Artificial Intelligence (XAI)Time Series Analysis and Forecasting

Cited by 38

Digital Twin-empowered Intelligent Computation Offloading for Edge Computing in the Era of 5G and Beyond: A State-of-the-Art Survey

Tran-Dang, H., & Kim, D.-S. (2025) · ICT Express

IoT and Edge/Fog ComputingDigital Transformation in Industry

Cited by 38

Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers

Geng, S., Gao, P., Chatterjee, M., Hori, C., Le Roux, J., Zhang, Y., Li, H., & Cherian, A. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence

Multimodal Machine Learning ApplicationsSpeech and dialogue systems

Cited by 38