Check computer science papers: citations, claims and retractions

Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection

Zhu, B., Jiang, Z., Zhou, X., Li, Z., & Yu, G. (2019) · arXiv

Advanced Neural Network Applications3D Shape Modeling and Analysis

Cited by 297

GPT is an effective tool for multilingual psychological text analysis

Rathje, S., Mirea, D.-M., Sucholutsky, I., Marjieh, R., Robertson, C. E., & Van Bavel, J. J. (2024) · Proceedings of the National Academy of Sciences

Sentiment Analysis and Opinion MiningComputational and Text Analysis Methods

Cited by 297

A Survey on Ransomware: Evolution, Taxonomy, and Defense Solutions

Oz, H., Aris, A., Levi, A., & Uluagac, A. S. (2022) · ACM Computing Surveys

Advanced Malware Detection TechniquesSecurity and Verification in Computing

Cited by 297

Analyzing the Behavior of Visual Question Answering Models

Agrawal, A., Batra, D., & Parikh, D. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

Multimodal Machine Learning ApplicationsVisual Attention and Saliency Detection

Cited by 297

DeepFake Detection for Human Face Images and Videos: A Survey

Malik, A., Kuribayashi, M., Abdullahi, S. M., & Khan, A. N. (2022) · IEEE Access

Digital Media Forensic DetectionFace recognition and analysis

Cited by 297

Volumetry of temporopolar, perirhinal, entorhinal and parahippocampal cortex from high-resolution MR images: considering the variability of the collateral sulcus

Pruessner, J. C. (2002) · Cerebral Cortex

Medical Image Segmentation TechniquesMemory and Neural Mechanisms

Cited by 297

Edgechain: an edge-IoT framework and prototype based on blockchain and smart contracts

Pan, J., Wang, J., Hester, A., Alqerm, I., Liu, Y., & Zhao, Y. (2019) · IEEE Internet of Things Journal

Blockchain Technology Applications and SecurityIoT and Edge/Fog Computing

Cited by 297

Local Binary Convolutional Neural Networks

Juefei-Xu, F., Boddeti, V. N., & Savvides, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsNeural Networks and Applications

Cited by 297

Quantum-channel capacity of very noisy channels

DiVincenzo, D. P., Shor, P. W., & Smolin, J. A. (1998) · Physical Review A

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 297

50 Years of Test (Un)fairness: Lessons for Machine Learning

Hutchinson, B., & Mitchell, M. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIDiscrimination and Equality Law

Cited by 297

Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification

Rodriguez, P., Cucurull, G., Gonzalez, J., Gonfaus, J. M., Nasrollahi, K., Moeslund, T. B., & Roca, F. X. (2022) · IEEE Transactions on Cybernetics

Emotion and Mood RecognitionFace recognition and analysis

Cited by 297

Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning

Yan, S., Yang, X., Huang, Z., Nie, E., Ding, Z., Li, Z., Ma, X., Bi, J., Kersting, K., Pan, J. Z., et al. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 297

Birnet: Brain image registration using dual-supervised fully convolutional networks

Fan, J., Cao, X., Yap, P.-T., & Shen, D. (2019) · Medical Image Analysis

Medical Image Segmentation TechniquesDomain Adaptation and Few-Shot Learning

Cited by 297

DeepSpeed Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale

Aminabadi, R. Y., Rajbhandari, S., Zhang, M., Awan, A. A., Li, C., Li, D., Zheng, E., Rasley, J., Smith, S., Ruwase, O., et al. (2022) · arXiv

Parallel Computing and Optimization TechniquesAdvanced Neural Network Applications

Cited by 297

Brain-to-text: decoding spoken phrases from phone representations in the brain

Herff, C., Heger, D., de Pesters, A., Telaar, D., Brunner, P., Schalk, G., & Schultz, T. (2015) · Frontiers in Neuroscience

Speech Recognition and SynthesisEEG and Brain-Computer Interfaces

Cited by 297

Double window optimisation for constant time visual SLAM

Strasdat, H., Davison, A. J., Montiel, J. M. M., & Konolige, K. (2011) · 2011 International Conference on Computer Vision

Robotics and Sensor-Based Localization3D Surveying and Cultural Heritage

Cited by 296

Gate-count estimates for performing quantum chemistry on small quantum computers

Wecker, D., Bauer, B., Clark, B. K., Hastings, M. B., & Troyer, M. (2014) · Physical Review A

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 296

Debugging reinvented: asking and answering why and why not questions about program behavior

Ko, A. J., & Myers, B. A. (2008) · Proceedings of the 30th international conference on Software engineering

Software Testing and Debugging TechniquesSoftware Engineering Research

Cited by 296

A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)

Patil, R., & Gudivada, A. V. (2024) · Applied Sciences

Topic ModelingNatural Language Processing Techniques

Cited by 296

Order-Embeddings of Images and Language

Vendrov, I., Kiros, R., Fidler, S., & Urtasun, R. (2015) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 296

Computational Thinking

Denning, P. J., & Tedre, M. (2019) · The MIT Press eBooks

Teaching and Learning ProgrammingHistory of Computing Technologies

Cited by 296

The relationship between trust in AI and trustworthy machine learning technologies

Toreini, E., Aitken, M., Coopamootoo, K., Elliott, K., Zelaya, C. G., & van Moorsel, A. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 296

Unsupervised deep learning for optical flow estimation

Ren, Z., Yan, J., Ni, B., Liu, B., Yang, X., & Zha, H. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence

Advanced Vision and ImagingVideo Surveillance and Tracking Methods

Cited by 296

Status report on the second round of the NIST post-quantum cryptography standardization process

Moody, D., Alagic, G., Apon, D. C., Cooper, D. A., Dang, Q. H., Kelsey, J. M., Liu, Y.-K., Miller, C. A., Peralta, R. C., Perlner, R. A., et al. (2020)

Cryptography and Data SecurityCryptography and Residue Arithmetic

Cited by 296