Check computer science papers: citations, claims and retractions

HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W. W., Salakhutdinov, R., & Manning, C. D. (2018) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 97

Cognitive imperialism in artificial intelligence: counteracting bias with indigenous epistemologies

Ofosu-Asare, Y. (2025) · AI & SOCIETY

Ethics and Social Impacts of AIDecolonial Thought and Epistemologies

Cited by 97

An application to detect cyberbullying using machine learning and deep learning techniques

Raj, M., Singh, S., Solanki, K., & Selvanambi, R. (2022) · SN Computer Science

Hate Speech and Cyberbullying DetectionSpam and Phishing Detection

Cited by 97

All Are Not Equal: An Examination of the Economic Returns to Different Forms of Participation in Open Source Software Communities

Hann, I.-H., Roberts, J. A., & Slaughter, S. A. (2013) · Information Systems Research

Open Source Software InnovationsDigital Platforms and Economics

Cited by 97

Security, Risk and the Biometric State

Muller, B. (2010) · Routledge eBooks

Biometric Identification and SecurityGlobal Security and Public Health

Cited by 97

Three-dimensional DenseNet self-attention neural network for automatic detection of student’s engagement

Mehta, N. K., Prasad, S. S., Saurav, S., Saini, R., & Singh, S. (2022) · Applied Intelligence

Emotion and Mood RecognitionOnline Learning and Analytics

Cited by 97

Saturation Level of the Hopfield Model for Neural Network

Crisanti, A., Amit, D. J., & Gutfreund, H. (1986) · Europhysics Letters (EPL)

Neural Networks and ApplicationsTheoretical and Computational Physics

Cited by 97

Generation of High-Resolution Handwritten Digits with an Ion-Trap Quantum Computer

Rudolph, M. S., Toussaint, N. B., Katabarwa, A., Johri, S., Peropadre, B., & Perdomo-Ortiz, A. (2020) · arXiv

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 97

Meta reinforcement learning for sim-to-real domain adaptation

Arndt, K., Hazara, M., Ghadirzadeh, A., & Kyrki, V. (2020) · 2020 IEEE International Conference on Robotics and Automation (ICRA)

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 97

Evaluation of texture methods for image analysis

Sharma, M., & Singh, S. (2001) · The Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001

Image Retrieval and Classification TechniquesRadiomics and Machine Learning in Medical Imaging

Cited by 97

“I’m sorry to hear that”: Finding new biases in language models with a holistic descriptor dataset

Smith, E. M., Hall, M., Kambadur, M., Presani, E., & Williams, A. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Ethics and Social Impacts of AIHate Speech and Cyberbullying Detection

Cited by 97

From General to Specific: Informative Scene Graph Generation via Balance Adjustment

Guo, Y., Gao, L., Wang, X., Hu, Y., Xu, X., Lu, X., Shen, H. T., & Song, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 97

Comprehensive Privacy Analysis on Federated Recommender System Against Attribute Inference Attacks

Zhang, S., Yuan, W., & Yin, H. (2024) · IEEE Transactions on Knowledge and Data Engineering

Privacy-Preserving Technologies in DataRecommender Systems and Techniques

Cited by 97

DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection

Yan, Z., Zhang, Y., Yuan, X., Lyu, S., & Wu, B. (2023) · arXiv

Digital Media Forensic DetectionMisinformation and Its Impacts

Cited by 97

Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Chen, T., Zhang, R., & Hinton, G. (2022) · arXiv

Generative Adversarial Networks and Image SynthesisAdversarial Robustness in Machine Learning

Cited by 97

Exploring Frequency Adversarial Attacks for Face Forgery Detection

Jia, S., Ma, C., Yao, T., Yin, B., Ding, S., & Yang, X. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Digital Media Forensic DetectionAdversarial Robustness in Machine Learning

Cited by 97

ChatGPT-generated help produces learning gains equivalent to human tutor-authored help on mathematics skills

Pardos, Z. A., & Bhandari, S. (2024) · PLOS ONE

Intelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in Education

Cited by 97

Free-rider attacks on model aggregation in federated learning

Fraboni, Y., Vidal, R., & Lorenzi, M. (2020) · arXiv

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 97

OpenForensics: Large-Scale Challenging Dataset For Multi-Face Forgery Detection And Segmentation In-The-Wild

Le, T.-N., Nguyen, H. H., Yamagishi, J., & Echizen, I. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Digital Media Forensic DetectionFace recognition and analysis

Cited by 97

Efficient Nearest Neighbor Language Models

He, J., Neubig, G., & Berg-Kirkpatrick, T. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 97

Federated Learning Of Out-Of-Vocabulary Words

Chen, M., Mathews, R., Ouyang, T., & Beaufays, F. (2019) · arXiv

Privacy-Preserving Technologies in DataNatural Language Processing Techniques

Cited by 97

Learning Personalized Preference of Strong and Weak Ties for Social Recommendation

Wang, X., Hoi, S. C. H., Ester, M., Bu, J., & Chen, C. (2017) · Proceedings of the 26th International Conference on World Wide Web

Recommender Systems and TechniquesInformation Retrieval and Data Mining

Cited by 97

Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects

Torrents-Barrena, J., Piella, G., Masoller, N., Gratacós, E., Eixarch, E., Ceresa, M., & Ballester, M. Á. G. (2019) · Medical Image Analysis

Medical Image Segmentation TechniquesFetal and Pediatric Neurological Disorders

Cited by 97

Automatic quantitative analysis of spontaneous aphasic speech

Le, D., Licata, K., & Mower Provost, E. (2018) · Speech Communication

Speech Recognition and SynthesisVoice and Speech Disorders

Cited by 97