Check computer science papers: citations, claims and retractions

Object Detection Networks on Convolutional Feature Maps

Ren, S., He, K., Girshick, R., Zhang, X., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 454

GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

Ainslie, J., Lee-Thorp, J., de Jong, M., Zemlyanskiy, Y., Lebron, F., & Sanghai, S. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 454

The StarCraft Multi-Agent Challenge

Samvelyan, M., Rashid, T., Schroeder de Witt, C., Farquhar, G., Nardelli, N., Rudner, T. G. J., Hung, C.-M., Torr, P. H. S., Foerster, J., & Whiteson, S. (2019) · International Joint Conference on Autonomous Agents and Multiagent Systems

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 453

Towards Accurate Binary Convolutional Neural Network

Lin, X., Zhao, C., & Pan, W. (2017) · arXiv

Advanced Neural Network ApplicationsAdvanced Memory and Neural Computing

Cited by 453

Information based adaptive robotic exploration

Bourgault, F., Makarenko, A. A., Williams, S. B., Grocholsky, B., & Durrant-Whyte, H. F. (2003) · IEEE/RSJ International Conference on Intelligent Robots and System

Robotics and Sensor-Based LocalizationReinforcement Learning in Robotics

Cited by 453

Blockchain and the Law: The Rule of Code

De Filippi, P., & Wright, A. (2018) · Harvard University Press eBooks

Blockchain Technology Applications and SecurityDigital Transformation in Law

Cited by 453

Depth Transfer: Depth Extraction from Video Using Non-Parametric Sampling

Karsch, K., Liu, C., & Kang, S. B. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Advanced Vision and ImagingImage Processing Techniques and Applications

Cited by 453

A CNN-LSTM Approach to Human Activity Recognition

Mutegeki, R., & Han, D. S. (2020) · 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

Context-Aware Activity Recognition SystemsGait Recognition and Analysis

Cited by 452

An Android Application Sandbox system for suspicious software detection

Bläsing, T., Batyuk, L., Schmidt, A.-D., Camtepe, S. A., & Albayrak, S. (2010) · 2010 5th International Conference on Malicious and Unwanted Software

Advanced Malware Detection TechniquesSecurity and Verification in Computing

Cited by 452

The relationship of action research to human-computer interaction

Hayes, G. R. (2011) · ACM Transactions on Computer-Human Interaction

Innovative Human-Technology InteractionUsability and User Interface Design

Cited by 452

Using task context to improve programmer productivity

Kersten, M., & Murphy, G. C. (2006) · Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 452

Fine-Grained Activity Recognition by Aggregating Abstract Object Usage

Patterson, D. J., Fox, D., Kautz, H., & Philipose, M. (2005) · Ninth IEEE International Symposium on Wearable Computers (ISWC'05)

Context-Aware Activity Recognition SystemsBayesian Modeling and Causal Inference

Cited by 452

Artificial Intelligence, Machine Learning, Automation, Robotics, Future of Work and Future of Humanity: A Review and Research Agenda

Wang, W., & Siau, K. (2019) · Journal of Database Management

Ethics and Social Impacts of AIImpact of AI and Big Data on Business and Society

Cited by 452

Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search

Zhang, T., Kahn, G., Levine, S., & Abbeel, P. (2016) · 2016 IEEE International Conference on Robotics and Automation (ICRA)

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 452

Feature engineering strategies for credit card fraud detection

Correa Bahnsen, A., Aouada, D., Stojanovic, A., & Ottersten, B. (2016) · Expert Systems with Applications

Imbalanced Data Classification TechniquesPrivacy-Preserving Technologies in Data

Cited by 451

Decentralized Privacy Using Blockchain-Enabled Federated Learning in Fog Computing

Qu, Y., Gao, L., Luan, T. H., Xiang, Y., Yu, S., Li, B., & Zheng, G. (2020) · IEEE Internet of Things Journal

Blockchain Technology Applications and SecurityPrivacy-Preserving Technologies in Data

Cited by 451

Exploring uncertainty measures in deep networks for Multiple sclerosis lesion detection and segmentation

Nair, T., Precup, D., Arnold, D. L., & Arbel, T. (2020) · Medical Image Analysis

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 451

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

Zhang, Z., Hamadi, H. A., Damiani, E., Yeun, C. Y., & Taher, F. (2022) · IEEE Access

Explainable Artificial Intelligence (XAI)Network Security and Intrusion Detection

Cited by 450

Continuous Prediction of Spontaneous Affect from Multiple Cues and Modalities in Valence-Arousal Space

Nicolaou, M. A., Gunes, H., & Pantic, M. (2011) · IEEE Transactions on Affective Computing

Emotion and Mood RecognitionSentiment Analysis and Opinion Mining

Cited by 450

Toward a Unified Model of Information Security Policy Compliance1

Moody, G. D., Siponen, M., & Pahnila, S. (2018) · MIS Quarterly

Information and Cyber SecurityPrivacy, Security, and Data Protection

Cited by 450

DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics

Ran, X., Chen, H., Zhu, X., Liu, Z., & Chen, J. (2018) · IEEE INFOCOM 2018 - IEEE Conference on Computer Communications

IoT and Edge/Fog ComputingAdvanced Neural Network Applications

Cited by 450

Deep learning tools for the measurement of animal behavior in neuroscience

Mathis, M. W., & Mathis, A. (2020) · Current Opinion in Neurobiology

Human Pose and Action RecognitionAdvanced Neural Network Applications

Cited by 450

How blockchain improves the supply chain: Case study alimentary supply chain

Casado-Vara, R., Prieto, J., la Prieta, F. D., & Corchado, J. M. (2018) · Procedia Computer Science

Blockchain Technology Applications and SecurityFood Supply Chain Traceability

Cited by 450

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Yu, T., Quillen, D., He, Z., Julian, R., Narayan, A., Shively, H., Bellathur, A., Hausman, K., Finn, C., & Levine, S. (2019) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 450