Check computer science papers: citations, claims and retractions

Self-Correction Bench: Uncovering and Addressing the Self-Correction Blind Spot in Large Language Models

Tsui, K. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 29

CANDY: A Social Engineering Attack to Leak Information from Infotainment System

Costantino, G., La Marra, A., Martinelli, F., & Matteucci, I. (2018) · 2018 IEEE 87th Vehicular Technology Conference (VTC Spring)

Security and Verification in ComputingVehicular Ad Hoc Networks (VANETs)

Cited by 29

A Novel Algorithm for Quantized Particle Filtering With Multiple Degrading Sensors: Degradation Estimation and Target Tracking

Liu, Y., Wang, Z., Liu, C., Coombes, M., & Chen, W.-H. (2023) · IEEE Transactions on Industrial Informatics

Target Tracking and Data Fusion in Sensor NetworksInertial Sensor and Navigation

Cited by 29

AI Maintenance: A Robustness Perspective

Chen, P.-Y., & Das, P. (2023) · Computer

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 29

Multi-Scale Vehicle Detection in High-Resolution Aerial Images With Context Information

Li, X., Li, X., & Pan, H. (2020) · IEEE Access

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 29

Minoritization vs. Universalization: Lesbianism and Male Homosexuality in LCSH and LCC

Christensen, B. (2008) · KNOWLEDGE ORGANIZATION

Library Science and Information SystemsGender and Feminist Studies

Cited by 29

ModelShield: Adaptive and Robust Watermark Against Model Extraction Attack

Pang, K., Qi, T., Wu, C., Bai, M., Jiang, M., & Huang, Y. (2025) · IEEE Transactions on Information Forensics and Security

Adversarial Robustness in Machine LearningAdvanced Steganography and Watermarking Techniques

Cited by 29

On the Limitations of Reference-Free Evaluations of Generated Text

Deutsch, D., Dror, R., & Roth, D. (2022) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 29

Advanced Active Learning Strategies for Object Detection

Schmidt, S., Rao, Q., Tatsch, J., & Knoll, A. (2020) · 2020 IEEE Intelligent Vehicles Symposium (IV)

Machine Learning and AlgorithmsAdvanced Neural Network Applications

Cited by 29

Combinatorial Optimization on Gate Model Quantum Computers: A Survey

Zahedinejad, E., & Zaribafiyan, A. (2017) · arXiv

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 29

Investigating the Limitations of Transformers with Simple Arithmetic Tasks

Nogueira, R., Jiang, Z., & Lin, J. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 29

A Fully Automated System Using A Convolutional Neural Network to Predict Renal Allograft Rejection: Extra-validation with Giga-pixel Immunostained Slides

Kim, Y.-G., Choi, G., Go, H., Cho, Y., Lee, H., Lee, A.-R., Park, B., & Kim, N. (2019) · Scientific Reports

AI in cancer detectionAdvanced Neural Network Applications

Cited by 29

Shaping new norms for AI

Baronchelli, A. (2024) · Philosophical Transactions of the Royal Society B: Biological Sciences

Ethics and Social Impacts of AIInnovation, Technology, and Society

Cited by 29

Exploiting Answer Set Programming with External Sources for Meta-Interpretive Learning

KAMINSKI, T., EITER, T., & INOUE, K. (2018) · Theory and Practice of Logic Programming

Logic, Reasoning, and KnowledgeAI-based Problem Solving and Planning

Cited by 29

Fact-checking the output of large language models via token-level uncertainty quantification

Fadeeva, E., Rubashevskii, A., Shelmanov, A., Petrakov, S., Li, H., Mubarak, H., Tsymbalov, E., Kuzmin, G., Panchenko, A., Baldwin, T., et al. (2024) · Findings of the Association for Computational Linguistics ACL 2024

Topic ModelingNatural Language Processing Techniques

Cited by 29

PersistBench: When Should Long-Term Memories Be Forgotten by LLMs?

Pulipaka, S., Chen, O., Sharma, M., Bajwa, T. S., Raina, V., & Sheth, I. (2026) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 29

FPGA based Deep Learning Models for Object Detection and Recognition Comparison of Object Detection Comparison of object detection models using FPGA

Kaarmukilan, S. P., Poddar, S., & Thomas K., A. (2020) · 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)

Advanced Neural Network ApplicationsEmbedded Systems and FPGA Applications

Cited by 29

Fred Jelinek

Liberman, M. (2010) · Computational Linguistics

Speech Recognition and SynthesisNatural Language Processing Techniques

Cited by 29

Beyond Mono to Binaural: Generating Binaural Audio from Mono Audio with Depth and Cross Modal Attention

Parida, K. K., Srivastava, S., & Sharma, G. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Speech and Audio ProcessingMusic and Audio Processing

Cited by 29

Forming Effective Human-AI Teams: Building Machine Learning Models that Complement the Capabilities of Multiple Experts

Hemmer, P., Schellhammer, S., Vössing, M., Jakubik, J., & Satzger, G. (2022) · Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence

Machine Learning and Data ClassificationMachine Learning and Algorithms

Cited by 29

LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks

Bai, Y., Tu, S., Zhang, J., Peng, H., Wang, X., Lv, X., Cao, S., Xu, J., Hou, L., Dong, Y., et al. (2025) · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingMultimodal Machine Learning Applications

Cited by 29

Towards Evaluating Narrative Quality In Student Writing

Somasundaran, S., Flor, M., Chodorow, M., Molloy, H., Gyawali, B., & McCulla, L. (2018) · Transactions of the Association for Computational Linguistics

Natural Language Processing TechniquesSentiment Analysis and Opinion Mining

Cited by 29

At Human Speed: Deep Reinforcement Learning with Action Delay

Firoiu, V., Ju, T., & Tenenbaum, J. (2018) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 29

DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models

Chuang, Y.-S., Xie, Y., Luo, H., Kim, Y., Glass, J., & He, P. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 29