Check computer science papers: citations, claims and retractions

Understanding and characterizing insights: how do people gain insights using information visualization?

Yi, J. S., Kang, Y.-A., Stasko, J. T., & Jacko, J. A. (2008) · Proceedings of the 2008 Workshop on BEyond time and errors: novel evaLuation methods for Information Visualization

Data Visualization and AnalyticsVisual and Cognitive Learning Processes

Cited by 175

Blockchain government - a next form of infrastructure for the twenty-first century

Jun, M. (2018) · Journal of Open Innovation: Technology, Market, and Complexity

Blockchain Technology Applications and SecurityE-Government and Public Services

Cited by 175

Internet Traffic Classification by Aggregating Correlated Naive Bayes Predictions

Zhang, J., Chen, C., Xiang, Y., Zhou, W., & Xiang, Y. (2013) · IEEE Transactions on Information Forensics and Security

Internet Traffic Analysis and Secure E-votingText and Document Classification Technologies

Cited by 175

Collaboration Through Open Superposition: A Theory of the Open Source Way1

Howison, J., & Crowston, K. (2014) · MIS Quarterly

Open Source Software InnovationsSoftware Engineering Techniques and Practices

Cited by 175

Access control in Internet-of-Things: A survey

Ravidas, S., Lekidis, A., Paci, F., & Zannone, N. (2019) · Journal of Network and Computer Applications

Access Control and TrustIoT and Edge/Fog Computing

Cited by 175

NAM: Normalization-based Attention Module

Liu, Y., Shao, Z., Teng, Y., & Hoffmann, N. (2021) · arXiv

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 175

Making AI Forget You: Data Deletion in Machine Learning

Ginart, A., Guan, M. Y., Valiant, G., & Zou, J. (2019) · arXiv

Privacy-Preserving Technologies in DataMachine Learning and Data Classification

Cited by 175

The Distinct Wrong of Deepfakes

de Ruiter, A. (2021) · Philosophy & Technology

Ethics and Social Impacts of AIPhilosophical Ethics and Theory

Cited by 175

Should AI models be explainable to clinicians?

Abgrall, G., Holder, A. L., Chelly Dagdia, Z., Zeitouni, K., & Monnet, X. (2024) · Critical Care

Explainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare and Education

Cited by 175

The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection

Tsintotas, K. A., Bampis, L., & Gasteratos, A. (2022) · IEEE Transactions on Intelligent Transportation Systems

Robotics and Sensor-Based LocalizationAdvanced Image and Video Retrieval Techniques

Cited by 175

A Study on Immersion and Presence of a Portable Hand Haptic System for Immersive Virtual Reality

Kim, M., Jeon, C., & Kim, J. (2017) · Sensors

Virtual Reality Applications and ImpactsTeleoperation and Haptic Systems

Cited by 175

AutoTopography: what can physical mementos tell us about digital memories?

Petrelli, D., Whittaker, S., & Brockmeier, J. (2008) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Innovative Human-Technology InteractionNostalgia and Consumer Behavior

Cited by 175

Best Match: New relevance search for PubMed

Fiorini, N., Canese, K., Starchenko, G., Kireev, E., Kim, W., Miller, V., Osipov, M., Kholodov, M., Ismagilov, R., Mohan, S., et al. (2018) · PLOS Biology

Information Retrieval and Search BehaviorRecommender Systems and Techniques

Cited by 175

MedCPT: Contrastive Pre-trained Transformers with large-scale PubMed search logs for zero-shot biomedical information retrieval

Jin, Q., Kim, W., Chen, Q., Comeau, D. C., Yeganova, L., Wilbur, W. J., & Lu, Z. (2023) · Bioinformatics

Information Retrieval and Search BehaviorBiomedical Text Mining and Ontologies

Cited by 175

RIFD-CNN: Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection

Cheng, G., Zhou, P., & Han, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 175

User-Guided Segmentation of Multi-modality Medical Imaging Datasets with ITK-SNAP

Yushkevich, P. A., Pashchinskiy, A., Oguz, I., Mohan, S., Schmitt, J. E., Stein, J. M., Zukić, D., Vicory, J., McCormick, M., Yushkevich, N., et al. (2019) · Neuroinformatics

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 175

Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning

Garcez, A. D., Gori, M., Lamb, L. C., Serafini, L., Spranger, M., & Tran, S. N. (2019) · arXiv

Explainable Artificial Intelligence (XAI)Logic, Reasoning, and Knowledge

Cited by 175

AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

Nair, A., Gupta, A., Dalal, M., & Levine, S. (2020) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 175

Position Information in Transformers: An Overview

Dufter, P., Schmitt, M., & Schütze, H. (2022) · Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 175

Emotion classification for short texts: an improved multi-label method

Liu, X., Shi, T., Zhou, G., Liu, M., Yin, Z., Yin, L., & Zheng, W. (2023) · Humanities and Social Sciences Communications

Sentiment Analysis and Opinion MiningText and Document Classification Technologies

Cited by 175

GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection

Zhang, S., Yin, H., Chen, T., Hung, Q. V. N., Huang, Z., & Cui, L. (2020) · Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

Recommender Systems and TechniquesSpam and Phishing Detection

Cited by 175

Causal Attention for Vision-Language Tasks

Yang, X., Zhang, H., Qi, G., & Cai, J. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsVisual Attention and Saliency Detection

Cited by 175

User performance in relation to 3D input device design

Zhai, S. (1998) · ACM SIGGRAPH Computer Graphics

Interactive and Immersive DisplaysUsability and User Interface Design

Cited by 175

Superneurons: dynamic GPU memory management for training deep neural networks

Wang, L., Ye, J., Zhao, Y., Wu, W., Li, A., Song, S. L., Xu, Z., & Kraska, T. (2018) · Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

Parallel Computing and Optimization TechniquesAdvanced Neural Network Applications

Cited by 175