Check computer science papers: citations, claims and retractions

A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

Sahoo, P., Singh, A. K., Saha, S., Jain, V., Mondal, S., & Chadha, A. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 288

Function Optimization using Connectionist Reinforcement Learning Algorithms

WILLIAMS, R. J., & PENG, J. (1991) · Connection Science

Reinforcement Learning in RoboticsNeural Networks and Applications

Cited by 288

Objects as Points

Zhou, X., Wang, D., & Krähenbühl, P. (2019) · arXiv

Advanced Neural Network ApplicationsEmbedded Systems Design Techniques

Cited by 288

Sybil: a validated deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography

Mikhael, P. G., Wohlwend, J., Yala, A., Karstens, L., Xiang, J., Takigami, A. K., Bourgouin, P. P., Chan, P., Mrah, S., Amayri, W., et al. (2023) · Journal of Clinical Oncology

AI in cancer detectionLung Cancer Diagnosis and Treatment

Cited by 288

A multidisciplinary survey on discrimination analysis

Romei, A., & Ruggieri, S. (2014) · The Knowledge Engineering Review

Ethics and Social Impacts of AINames, Identity, and Discrimination Research

Cited by 288

LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework

Wöllmer, M., Kaiser, M., Eyben, F., Schuller, B., & Rigoll, G. (2013) · Image and Vision Computing

Emotion and Mood RecognitionSpeech and Audio Processing

Cited by 288

Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks

Tellez, D., Balkenhol, M., Otte-Höller, I., van de Loo, R., Vogels, R., Bult, P., Wauters, C., Vreuls, W., Mol, S., Karssemeijer, N., et al. (2018) · IEEE Transactions on Medical Imaging

AI in cancer detectionCell Image Analysis Techniques

Cited by 288

Lower Bound Theory of Nonzero Entries in Solutions of $\ell_2$-$\ell_p$ Minimization

Chen, X., Xu, F., & Ye, Y. (2010) · SIAM Journal on Scientific Computing

Sparse and Compressive Sensing TechniquesStatistical Methods and Inference

Cited by 288

Challenges in building intelligent open-domain dialog systems

Huang, M., Zhu, X., & Gao, J. (2020) · ACM Transactions on Information Systems

Speech and dialogue systemsAI in Service Interactions

Cited by 288

Minimal universal two-qubit controlled-NOT-based circuits

Shende, V. V., Markov, I. L., & Bullock, S. S. (2004) · Physical Review A

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 288

Federated learning in a medical context: A systematic literature review

Pfitzner, B., Steckhan, N., & Arnrich, B. (2021) · ACM Transactions on Internet Technology

Privacy-Preserving Technologies in DataArtificial Intelligence in Healthcare and Education

Cited by 288

Representative Forgery Mining for Fake Face Detection

Wang, C., & Deng, W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Digital Media Forensic DetectionFace recognition and analysis

Cited by 288

The Solovay-Kitaev algorithm

Dawson, C. M., & Nielsen, M. A. (2006) · Quantum Information and Computation

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 288

Selecting the Number of States in Hidden Markov Models: Pragmatic Solutions Illustrated Using Animal Movement

Pohle, J., Langrock, R., van Beest, F. M., & Schmidt, N. M. (2017) · Journal of Agricultural, Biological and Environmental Statistics

Bayesian Methods and Mixture ModelsStatistical Methods and Inference

Cited by 288

Geography-Aware Self-Supervised Learning

Ayush, K., Uzkent, B., Meng, C., Tanmay, K., Burke, M., Lobell, D., & Ermon, S. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Domain Adaptation and Few-Shot LearningRemote-Sensing Image Classification

Cited by 288

Deep CNN-LSTM With Self-Attention Model for Human Activity Recognition Using Wearable Sensor

Khatun, M. A., Yousuf, M. A., Ahmed, S., Uddin, M. Z., Alyami, S. A., Al-Ashhab, S., Akhdar, H. F., Khan, A., Azad, A., & Moni, M. A. (2022) · IEEE Journal of Translational Engineering in Health and Medicine

Context-Aware Activity Recognition SystemsGait Recognition and Analysis

Cited by 288

Social determinants of health in electronic health records and their impact on analysis and risk prediction: A systematic review

Chen, M., Tan, X., & Padman, R. (2020) · Journal of the American Medical Informatics Association

Machine Learning in HealthcareElectronic Health Records Systems

Cited by 287

Information Flows in Causal Networks

AY, N., & POLANI, D. (2008) · Advances in Complex Systems

Bayesian Modeling and Causal InferencePhilosophy of Biology and Scientific Explanation

Cited by 287

Demonstration of fault-tolerant universal quantum gate operations

Postler, L., Heuβen, S., Pogorelov, I., Rispler, M., Feldker, T., Meth, M., Marciniak, C. D., Stricker, R., Ringbauer, M., Blatt, R., et al. (2022) · Nature

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 287

Are Currently Available Wearable Devices for Activity Tracking and Heart Rate Monitoring Accurate, Precise, and Medically Beneficial?

El-Amrawy, F., & Nounou, M. I. (2015) · Healthcare Informatics Research

Context-Aware Activity Recognition SystemsMobile Health and mHealth Applications

Cited by 287

Gender and Representation Bias in GPT-3 Generated Stories

Lucy, L., & Bamman, D. (2021) · Proceedings of the Third Workshop on Narrative Understanding

Ethics and Social Impacts of AITopic Modeling

Cited by 287

The Impact of AI on Developer Productivity: Evidence from GitHub Copilot

Peng, S., Kalliamvakou, E., Cihon, P., & Demirer, M. (2023) · arXiv

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 287

Progressive Distillation for Fast Sampling of Diffusion Models

Salimans, T., & Ho, J. (2022) · arXiv

Generative Adversarial Networks and Image SynthesisAdversarial Robustness in Machine Learning

Cited by 287

Inner Monologue: Embodied Reasoning through Planning with Language Models

Huang, W., Xia, F., Xiao, T., Chan, H., Liang, J., Florence, P., Zeng, A., Tompson, J., Mordatch, I., Chebotar, Y., et al. (2022) · arXiv

AI-based Problem Solving and PlanningRobot Manipulation and Learning

Cited by 287