Check computer science papers: citations, claims and retractions

COGS: A Compositional Generalization Challenge Based on Semantic Interpretation

Kim, N., & Linzen, T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Natural Language Processing TechniquesTopic Modeling

Cited by 153

Expel: LLM agents are experiential learners

Zhao, A., Huang, D., Xu, Q., Lin, M., Liu, Y.-J., & Huang, G. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence

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

Cited by 153

Deep Reinforcement Learning for Mobile Edge Caching: Review, New Features, and Open Issues

Zhu, H., Cao, Y., Wang, W., Jiang, T., & Jin, S. (2018) · IEEE Network

Caching and Content DeliveryIoT and Edge/Fog Computing

Cited by 153

PipeDream: Fast and Efficient Pipeline Parallel DNN Training

Harlap, A., Narayanan, D., Phanishayee, A., Seshadri, V., Devanur, N., Ganger, G., & Gibbons, P. (2018) · arXiv

Parallel Computing and Optimization TechniquesStochastic Gradient Optimization Techniques

Cited by 153

An extensive study on pre-trained models for program understanding and generation

Zeng, Z., Tan, H., Zhang, H., Li, J., Zhang, Y., & Zhang, L. (2022) · Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis

Software Engineering ResearchTopic Modeling

Cited by 153

A pilot study of long-term monitoring of human movements in the home using accelerometry

Mathie, M. J., Coster, A. C. F., Lovell, N. H., Celler, B. G., Lord, S. R., & Tiedemann, A. (2004) · Journal of Telemedicine and Telecare

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

Cited by 153

In-IDE Code Generation from Natural Language: Promise and Challenges

Xu, F. F., Vasilescu, B., & Neubig, G. (2021) · arXiv

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 153

Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning

Gupta, A., Kumar, V., Lynch, C., Levine, S., & Hausman, K. (2019) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 153

A fuzzy approach to trust based access control in internet of things

Mahalle, P. N., Thakre, P. A., Prasad, N. R., & Prasad, R. (2013) · Wireless VITAE 2013

Access Control and TrustIoT and Edge/Fog Computing

Cited by 153

Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platforms

Ko, J. H., Na, T., Amir, M. F., & Mukhopadhyay, S. (2018) · 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

IoT and Edge/Fog ComputingAdvanced Neural Network Applications

Cited by 153

PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts

Zhu, K., Wang, J., Zhou, J., Wang, Z., Chen, H., Wang, Y., Yang, L., Ye, W., Zhang, Y., Gong, N. Z., et al. (2023) · arXiv

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 153

On the number of response regions of deep feed forward networks with piece-wise linear activations

Pascanu, R., Montufar, G., & Bengio, Y. (2013) · arXiv

Neural Networks and ApplicationsStochastic Gradient Optimization Techniques

Cited by 153

Collection and Processing of Data from Wrist Wearable Devices in Heterogeneous and Multiple-User Scenarios

De Arriba-Pérez, F., Caeiro-Rodríguez, M., & Santos-Gago, J. (2016) · Sensors

Context-Aware Activity Recognition SystemsIoT and Edge/Fog Computing

Cited by 153

Toward situated interventions for algorithmic equity: lessons from the field

Katell, M., Young, M., Dailey, D., Herman, B., Guetler, V., Tam, A., Bintz, C., Raz, D., & Krafft, P. M. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 153

AI art in architecture

Ploennigs, J., & Berger, M. (2023) · AI in Civil Engineering

Generative Adversarial Networks and Image SynthesisArchitecture and Computational Design

Cited by 153

Speech emotion recognition using fusion of three multi-task learning-based classifiers: HSF-DNN, MS-CNN and LLD-RNN

Yao, Z., Wang, Z., Liu, W., Liu, Y., & Pan, J. (2020) · Speech Communication

Emotion and Mood RecognitionSpeech Recognition and Synthesis

Cited by 152

Linear versus mel frequency cepstral coefficients for speaker recognition

Zhou, X., Garcia-Romero, D., Duraiswami, R., Espy-Wilson, C., & Shamma, S. (2011) · 2011 IEEE Workshop on Automatic Speech Recognition & Understanding

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 152

Federated learning: A collaborative effort to achieve better medical imaging models for individual sites that have small labelled datasets

Ng, D., Lan, X., Yao, M. M.-S., Chan, W. P., & Feng, M. (2021) · Quantitative Imaging in Medicine and Surgery

Privacy-Preserving Technologies in DataArtificial Intelligence in Healthcare and Education

Cited by 152

Human emotion recognition through short time Electroencephalogram (EEG) signals using Fast Fourier Transform (FFT)

Murugappan, M., & Murugappan, S. (2013) · 2013 IEEE 9th International Colloquium on Signal Processing and its Applications

Emotion and Mood RecognitionEEG and Brain-Computer Interfaces

Cited by 152

Discrimination, artificial intelligence, and algorithmic decision-making

Borgesius, F. Z. (2025) · arXiv

Ethics and Social Impacts of AIDiscrimination and Equality Law

Cited by 152

Efficient magic state factories with a catalyzed | C C Z ⟩ to 2 | T ⟩ transformation

Gidney, C., & Fowler, A. G. (2019) · Quantum

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 152

Virtual reality adoption during the COVID-19 pandemic: A uses and gratifications perspective

Ball, C., Huang, K.-T., & Francis, J. (2021) · Telematics and Informatics

Virtual Reality Applications and ImpactsTechnology Adoption and User Behaviour

Cited by 152

A New Take on Detecting Insider Threats: Exploring the Use of Hidden Markov Models

Rashid, T., Agrafiotis, I., & Nurse, J. R. C. (2016) · Proceedings of the 8th ACM CCS International Workshop on Managing Insider Security Threats

Network Security and Intrusion DetectionInformation and Cyber Security

Cited by 152

Safe Exploration in Markov Decision Processes

Moldovan, T. M., & Abbeel, P. (2012) · arXiv

Reinforcement Learning in RoboticsAdvanced Bandit Algorithms Research

Cited by 152