Check computer science papers: citations, claims and retractions

Do Language Models Know When They’re Hallucinating References?

Agrawal, A., Suzgun, M., Mackey, L., & Kalai, A. T. (2023) · arXiv

Topic ModelingMisinformation and Its Impacts

Cited by 37

CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents

Zhao, Q., Wang, J., Zhang, Y., Jin, Y., Zhu, K., Chen, H., & Xie, X. (2023) · arXiv

Multi-Agent Systems and NegotiationArtificial Intelligence Applications

Cited by 37

Being Bayesian about Categorical Probability

Joo, T., Chung, U., & Seo, M.-G. (2020) · arXiv

Adversarial Robustness in Machine LearningAdvanced Neural Network Applications

Cited by 37

Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Tseng, G., Cartuyvels, R., Zvonkov, I., Purohit, M., Rolnick, D., & Kerner, H. (2023) · arXiv

Remote-Sensing Image ClassificationRemote Sensing in Agriculture

Cited by 37

The Primacy Bias in Deep Reinforcement Learning

Nikishin, E., Schwarzer, M., D'Oro, P., Bacon, P.-L., & Courville, A. (2022) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 37

An intrinsic stopping criterion for committee-based active learning

Olsson, F., & Tomanek, K. (2009) · Proceedings of the Thirteenth Conference on Computational Natural Language Learning - CoNLL '09

Machine Learning and AlgorithmsNatural Language Processing Techniques

Cited by 37

Multicriteria Based Decision Making of DevOps Data Quality Assessment Challenges Using Fuzzy TOPSIS

Rafi, S., Yu, W., Akbar, M. A., Alsanad, A., & Gumaei, A. (2020) · IEEE Access

Data Quality and ManagementSoftware Engineering Techniques and Practices

Cited by 37

Accommodating Transformer onto FPGA: Coupling the Balanced Model Compression and FPGA-Implementation Optimization

Qi, P., Song, Y., Peng, H., Huang, S., Zhuge, Q., & Sha, E. H.-M. (2021) · Proceedings of the 2021 Great Lakes Symposium on VLSI

Embedded Systems Design TechniquesAdvanced Neural Network Applications

Cited by 37

A Ubiquitous Machine Learning Accelerator with Automatic Parallelization on FPGA

Wang, C., Gong, L., Li, X., & Zhou, X. (2020) · IEEE Transactions on Parallel and Distributed Systems

Embedded Systems Design TechniquesVLSI and FPGA Design Techniques

Cited by 37

Artificial Intelligence for Dermatopathology: Current Trends and the Road Ahead

Chen, S. B., & Novoa, R. A. (2022) · Seminars in Diagnostic Pathology

AI in cancer detectionArtificial Intelligence in Healthcare and Education

Cited by 37

Lightweight Privacy and Security Computing for Blockchained Federated Learning in IoT

Fan, M., Ji, K., Zhang, Z., Yu, H., & Sun, G. (2023) · IEEE Internet of Things Journal

Blockchain Technology Applications and SecurityPrivacy-Preserving Technologies in Data

Cited by 37

LLMSense: Harnessing LLMs for High-level Reasoning Over Spatiotemporal Sensor Traces

Ouyang, X., & Srivastava, M. (2024) · arXiv

Context-Aware Activity Recognition SystemsIoT and Edge/Fog Computing

Cited by 37

“Your friendly AI assistant”: the anthropomorphic self-representations of ChatGPT and its implications for imagining AI

van Es, K., & Nguyen, D. (2025) · AI & SOCIETY

Ethics and Social Impacts of AICybernetics and Technology in Society

Cited by 37

Textual Feature Extraction Using Ant Colony Optimization for Hate Speech Classification

Gite, S., Patil, S., Dharrao, D., Yadav, M., Basak, S., Rajendran, A., & Kotecha, K. (2023) · Big Data and Cognitive Computing

Text and Document Classification TechnologiesSentiment Analysis and Opinion Mining

Cited by 37

A Systematic Review of Domains, Techniques, Delivery Modes and Validation Methods for Intelligent Tutoring Systems

Soofi, A. A., & Uddin, M. (2019) · International Journal of Advanced Computer Science and Applications

Intelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in Education

Cited by 37

BayBFed: Bayesian Backdoor Defense for Federated Learning

Kumari, K., Rieger, P., Fereidooni, H., Jadliwala, M., & Sadeghi, A.-R. (2023) · 2023 IEEE Symposium on Security and Privacy (SP)

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 37

The effect of System Response Time on interactive computer aided problem solving

Goodman, T., & Spence, R. (1978) · ACM SIGGRAPH Computer Graphics

Computer Graphics and Visualization TechniquesUsability and User Interface Design

Cited by 37

Systems for big-graphs

Khan, A., & Elnikety, S. (2014) · Proceedings of the VLDB Endowment

Graph Theory and AlgorithmsAdvanced Database Systems and Queries

Cited by 37

Wave Communication across Regular Lattices

Hein, B., & Tanner, G. (2009) · Physical Review Letters

Quantum Computing Algorithms and ArchitectureQuantum Mechanics and Applications

Cited by 37

Automating chaos experiments in production

Basiri, A., Hochstein, L., Jones, N., & Tucker, H. (2019) · 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)

Software System Performance and ReliabilityDistributed systems and fault tolerance

Cited by 37

The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientation

Hartmann, J., Schwenzow, J., & Witte, M. (2023) · arXiv

Ethics and Social Impacts of AISocial Media and Politics

Cited by 37

Handling sensing failures in autonomous mobile robots

Murphy, R. R., & Hershberger, D. (1999) · The International Journal of Robotics Research

Robotics and Sensor-Based LocalizationAI-based Problem Solving and Planning

Cited by 37

A Survey on Hallucination in Large Vision-Language Models

Liu, H., Xue, W., Chen, Y., Chen, D., Zhao, X., Wang, K., Hou, L., Li, R., & Peng, W. (2024) · arXiv

Multimodal Machine Learning ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 37

A Review of Homomorphic Encryption Libraries for Secure Computation

Sathya, S. S., Vepakomma, P., Raskar, R., Ramachandra, R., & Bhattacharya, S. (2018) · arXiv

Cryptography and Data SecurityCloud Data Security Solutions

Cited by 37