Check computer science papers: citations, claims and retractions

Black-box Detection of Backdoor Attacks with Limited Information and Data

Dong, Y., Yang, X., Deng, Z., Pang, T., Xiao, Z., Su, H., & Zhu, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Adversarial Robustness in Machine LearningDigital Media Forensic Detection

Cited by 92

EMO: Emote Portrait Alive - Generating Expressive Portrait Videos with Audio2Video Diffusion Model under Weak Conditions

Tian, L., Wang, Q., Zhang, B., & Bo, L. (2024) · arXiv

Generative Adversarial Networks and Image SynthesisFace recognition and analysis

Cited by 92

Blockchain-Supported Federated Learning for Trustworthy Vehicular Networks

Otoum, S., Al Ridhawi, I., & Mouftah, H. T. (2020) · GLOBECOM 2020 - 2020 IEEE Global Communications Conference

Blockchain Technology Applications and SecurityPrivacy-Preserving Technologies in Data

Cited by 92

A Deep Learning Perspective on the Origin of Facial Expressions

Breuer, R., & Kimmel, R. (2017) · arXiv

Emotion and Mood RecognitionFace recognition and analysis

Cited by 92

Semantic Robustness of Models of Source Code

Henkel, J., Ramakrishnan, G., Wang, Z., Albarghouthi, A., Jha, S., & Reps, T. (2022) · 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

Adversarial Robustness in Machine LearningSoftware Engineering Research

Cited by 92

Compiling KB-sized machine learning models to tiny IoT devices

Gopinath, S., Ghanathe, N., Seshadri, V., & Sharma, R. (2019) · Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation

Numerical Methods and AlgorithmsIoT and Edge/Fog Computing

Cited by 92

Ditto: Building Digital Twins of Articulated Objects from Interaction

Jiang, Z., Hsu, C.-C., & Zhu, Y. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

3D Shape Modeling and AnalysisRobot Manipulation and Learning

Cited by 92

Mitigating face recognition bias via group adaptive classifier

Gong, S., Liu, X., & Jain, A. K. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Face recognition and analysisFace and Expression Recognition

Cited by 92

Brain and lesion segmentation in multiple sclerosis using fully convolutional neural networks: A large-scale study

Gabr, R. E., Coronado, I., Robinson, M., Sujit, S. J., Datta, S., Sun, X., Allen, W. J., Lublin, F. D., Wolinsky, J. S., & Narayana, P. A. (2020) · Multiple Sclerosis Journal

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 92

Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback

Zhang, X., Zhang, Y., Sun, H., Feng, K., Lu, C., Yang, C., & Meng, H. (2025) · arXiv

Topic ModelingReinforcement Learning in Robotics

Cited by 92

Thinking like a naturalist: Enhancing computer vision of citizen science images by harnessing contextual data

Terry, J. C. D., Roy, H. E., & August, T. A. (2020) · Methods in Ecology and Evolution

Advanced Neural Network ApplicationsSmart Agriculture and AI

Cited by 92

Break the Loop: Gender Imbalance in Music Recommenders

Ferraro, A., Serra, X., & Bauer, C. (2021) · Proceedings of the 2021 Conference on Human Information Interaction and Retrieval

Recommender Systems and TechniquesEthics and Social Impacts of AI

Cited by 92

Touchscreen typing-pattern analysis for detecting fine motor skills decline in early-stage Parkinson's disease

Iakovakis, D., Hadjidimitriou, S., Charisis, V., Bostantzopoulou, S., Katsarou, Z., & Hadjileontiadis, L. J. (2018) · Scientific Reports

Context-Aware Activity Recognition SystemsArtificial Intelligence in Healthcare

Cited by 92

Computational Creativity: Coming of Age

Colton, S., de Mántaras, R. L., & Stock, O. (2009) · AI Magazine

Artificial Intelligence in GamesCreativity in Education and Neuroscience

Cited by 92

AI chatbots in programming education: Students’ use in a scientific computing course and consequences for learning

Groothuijsen, S., van den Beemt, A., Remmers, J. C., & van Meeuwen, L. W. (2024) · Computers and Education: Artificial Intelligence

Teaching and Learning ProgrammingArtificial Intelligence in Education

Cited by 92

Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography

Alves, N., Schuurmans, M., Litjens, G., Bosma, J. S., Hermans, J., & Huisman, H. (2022) · Cancers

AI in cancer detectionMedical Image Segmentation Techniques

Cited by 92

Bioinspired Intelligent Algorithm and Its Applications for Mobile Robot Control: A Survey

Ni, J., Wu, L., Fan, X., & Yang, S. X. (2016) · Computational Intelligence and Neuroscience

Metaheuristic Optimization Algorithms ResearchControl and Dynamics of Mobile Robots

Cited by 92

Dynamic Difference Learning With Spatio–Temporal Correlation for Deepfake Video Detection

Yin, Q., Lu, W., Li, B., & Huang, J. (2023) · IEEE Transactions on Information Forensics and Security

Digital Media Forensic DetectionGenerative Adversarial Networks and Image Synthesis

Cited by 92

Wearable Sensor-Based Human Activity Recognition with Hybrid Deep Learning Model

Luwe, Y. J., Lee, C. P., & Lim, K. M. (2022) · Informatics

Context-Aware Activity Recognition SystemsGait Recognition and Analysis

Cited by 92

RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs

Chaudhari, S., Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., Deshpande, A., & Castro da Silva, B. (2026) · ACM Computing Surveys

Reinforcement Learning in RoboticsNatural Language Processing Techniques

Cited by 92

SIREN: A Simulation Framework for Understanding the Effects of Recommender Systems in Online News Environments

Bountouridis, D., Harambam, J., Makhortykh, M., Marrero, M., Tintarev, N., & Hauff, C. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency

Recommender Systems and TechniquesEthics and Social Impacts of AI

Cited by 92

JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs

Ke, P., Ji, H., Ran, Y., Cui, X., Wang, L., Song, L., Zhu, X., & Huang, M. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

Topic ModelingAdvanced Graph Neural Networks

Cited by 92

Modeling Ambiguity, Subjectivity, and Diverging Viewpoints in Opinion Question Answering Systems

Wan, M., & McAuley, J. (2016) · 2016 IEEE 16th International Conference on Data Mining (ICDM)

Expert finding and Q&A systemsSentiment Analysis and Opinion Mining

Cited by 92

MultiSpeech: Multi-Speaker Text to Speech with Transformer

Chen, M., Tan, X., Ren, Y., Xu, J., Sun, H., Zhao, S., & Qin, T. (2020) · Interspeech 2020

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 92