Check computer science papers: citations, claims and retractions

Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning

Rudin, N., Hoeller, D., Reist, P., & Hutter, M. (2021) · arXiv

Reinforcement Learning in RoboticsRobotic Locomotion and Control

Cited by 296

Barometric Pressure and Triaxial Accelerometry-Based Falls Event Detection

Bianchi, F., Redmond, S. J., Narayanan, M. R., Cerutti, S., & Lovell, N. H. (2010) · IEEE Transactions on Neural Systems and Rehabilitation Engineering

Context-Aware Activity Recognition SystemsBalance, Gait, and Falls Prevention

Cited by 296

Improving Neural Machine Translation Models with Monolingual Data

Sennrich, R., Haddow, B., & Birch, A. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Natural Language Processing TechniquesTopic Modeling

Cited by 296

Creating and benchmarking a new dataset for physical activity monitoring

Reiss, A., & Stricker, D. (2012) · Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments

Context-Aware Activity Recognition SystemsPhysical Activity and Health

Cited by 295

Deep learning for hybrid 5G services in mobile edge computing systems: learn from a digital twin

Dong, R., She, C., Hardjawana, W., Li, Y., & Vucetic, B. (2019) · IEEE Transactions on Wireless Communications

IoT and Edge/Fog ComputingAdvanced Wireless Network Optimization

Cited by 295

A survey of edge computing-based designs for IoT security

Sha, K., Yang, T. A., Wei, W., & Davari, S. (2020) · Digital Communications and Networks

IoT and Edge/Fog ComputingNetwork Security and Intrusion Detection

Cited by 295

Novel Deep Learning-Enabled LSTM Autoencoder Architecture for Discovering Anomalous Events From Intelligent Transportation Systems

Ashraf, J., Bakhshi, A. D., Moustafa, N., Khurshid, H., Javed, A., & Beheshti, A. (2021) · IEEE Transactions on Intelligent Transportation Systems

Network Security and Intrusion DetectionVehicular Ad Hoc Networks (VANETs)

Cited by 295

The WY representation for products of householder matrices

Bischof, C., & Van Loan, C. (1987) · SIAM Journal on Scientific and Statistical Computing

Matrix Theory and AlgorithmsParallel Computing and Optimization Techniques

Cited by 295

Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models

Marks, S., Rager, C., Michaud, E. J., Belinkov, Y., Bau, D., & Mueller, A. (2024) · arXiv

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 295

Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective

Sarker, I. H. (2021) · SN Computer Science

Network Security and Intrusion DetectionAdversarial Robustness in Machine Learning

Cited by 295

An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTSCAN Imagery

Magesh, P. R., Myloth, R. D., & Tom, R. J. (2020) · Computers in Biology and Medicine

Explainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare

Cited by 295

Random Walks: A Review of Algorithms and Applications

Xia, F., Liu, J., Nie, H., Fu, Y., Wan, L., & Kong, X. (2020) · IEEE Transactions on Emerging Topics in Computational Intelligence

Quantum Computing Algorithms and ArchitectureComplex Network Analysis Techniques

Cited by 295

Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects

Zhang, K., Wen, Q., Zhang, C., Cai, R., Jin, M., Liu, Y., Zhang, J. Y., Liang, Y., Pang, G., Song, D., et al. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Time Series Analysis and ForecastingDomain Adaptation and Few-Shot Learning

Cited by 295

Compositional Visual Generation with Composable Diffusion Models

Liu, N., Li, S., Du, Y., Torralba, A., & Tenenbaum, J. B. (2022) · arXiv

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 295

Event Detection and Tracking in Social Streams

Sayyadi, H., Hurst, M., & Maykov, A. (2009) · Proceedings of the International AAAI Conference on Web and Social Media

Topic ModelingAdvanced Text Analysis Techniques

Cited by 295

TinyML-Enabled Frugal Smart Objects: Challenges and Opportunities

Sanchez-Iborra, R., & Skarmeta, A. F. (2020) · IEEE Circuits and Systems Magazine

IoT and Edge/Fog ComputingInternet of Things and AI

Cited by 295

Self-supervised learning: A succinct review

Rani, V., Nabi, S. T., Kumar, M., Mittal, A., & Kumar, K. (2023) · Archives of Computational Methods in Engineering

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 295

A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

Lanctot, M., Zambaldi, V., Gruslys, A., Lazaridou, A., Tuyls, K., Perolat, J., Silver, D., & Graepel, T. (2017) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 295

Implementing the quantum random walk

Travaglione, B. C., & Milburn, G. J. (2002) · Physical Review A

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 295

The weighted majority algorithm

Littlestone, N., & Warmuth, M. K. (1989) · 30th Annual Symposium on Foundations of Computer Science

Machine Learning and AlgorithmsAdvanced Bandit Algorithms Research

Cited by 295

Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain

Rosenberg, I., Shabtai, A., Elovici, Y., & Rokach, L. (2022) · ACM Computing Surveys

Adversarial Robustness in Machine LearningAdvanced Malware Detection Techniques

Cited by 295

π₀: A Vision-Language-Action Flow Model for General Robot Control

Black, K., Brown, N., Driess, D., Esmail, A., Equi, M., Finn, C., Fusai, N., Groom, L., Hausman, K., Ichter, B., et al. (2025) · Robotics: Science and Systems XXI

AI-based Problem Solving and PlanningRobot Manipulation and Learning

Cited by 295

ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory

Ouyang, S., Yan, J., Hsu, I.-H., Chen, Y., Jiang, K., Wang, Z., Han, R., Le, L. T., Daruki, S., Tang, X., et al. (2025) · arXiv

AI-based Problem Solving and PlanningDomain Adaptation and Few-Shot Learning

Cited by 295

Towards a critical race methodology in algorithmic fairness

Hanna, A., Denton, R., Smart, A., & Smith-Loud, J. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIRace, Genetics, and Society

Cited by 295