Check computer science papers: citations, claims and retractions

Understanding User Beliefs About Algorithmic Curation in the Facebook News Feed

Rader, E., & Gray, R. (2015) · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

Ethics and Social Impacts of AIMisinformation and Its Impacts

Cited by 386

Hierarchical Multiscale Recurrent Neural Networks

Chung, J., Ahn, S., & Bengio, Y. (2016) · arXiv

Neural Networks and ApplicationsTopic Modeling

Cited by 386

C-RNN-GAN: Continuous recurrent neural networks with adversarial training

Mogren, O. (2016) · arXiv

Generative Adversarial Networks and Image SynthesisMusic Technology and Sound Studies

Cited by 386

Better, Nicer, Clearer, Fairer: A Critical Assessment of the Movement for Ethical Artificial Intelligence and Machine Learning

Greene, D., Hoffmann, A. L., & Stark, L. (2019) · Proceedings of the Annual Hawaii International Conference on System Sciences

Ethics and Social Impacts of AIEthics in Business and Education

Cited by 386

A Structured Review of the Validity of BLEU

Reiter, E. (2018) · Computational Linguistics

Natural Language Processing TechniquesTopic Modeling

Cited by 386

Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement

Lee, J., Mansimov, E., & Cho, K. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Natural Language Processing TechniquesTopic Modeling

Cited by 386

Speaker recognition based on deep learning: An overview

Bai, Z., & Zhang, X.-L. (2021) · Neural Networks

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 386

Mixture-of-Agents Enhances Large Language Model Capabilities

Wang, J., Wang, J., Athiwaratkun, B., Zhang, C., & Zou, J. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 386

Sim-to-Real Robot Learning from Pixels with Progressive Nets

Rusu, A. A., Vecerik, M., Rothörl, T., Heess, N., Pascanu, R., & Hadsell, R. (2016) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 385

Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy

Li, E., Zhou, Z., & Chen, X. (2018) · Proceedings of the 2018 Workshop on Mobile Edge Communications

IoT and Edge/Fog ComputingAdvanced Neural Network Applications

Cited by 385

Blockchain Based Data Integrity Service Framework for IoT Data

Liu, B., Yu, X. L., Chen, S., Xu, X., & Zhu, L. (2017) · 2017 IEEE International Conference on Web Services (ICWS)

Blockchain Technology Applications and SecurityCloud Data Security Solutions

Cited by 385

The first facial expression recognition and analysis challenge

Valstar, M. F., Jiang, B., Mehu, M., Pantic, M., & Scherer, K. (2011) · Face and Gesture 2011

Emotion and Mood RecognitionFace recognition and analysis

Cited by 385

Deep Stereo Using Adaptive Thin Volume Representation With Uncertainty Awareness

Cheng, S., Xu, Z., Zhu, S., Li, Z., Li, L. E., Ramamoorthi, R., & Su, H. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Vision and Imaging3D Shape Modeling and Analysis

Cited by 385

Assessing BERT's Syntactic Abilities

Goldberg, Y. (2019) · arXiv

Natural Language Processing TechniquesSyntax, Semantics, Linguistic Variation

Cited by 385

Mathematical Capabilities of ChatGPT

Frieder, S., Pinchetti, L., Chevalier, A., Griffiths, R.-R., Salvatori, T., Lukasiewicz, T., Petersen, P. C., & Berner, J. (2023) · arXiv

Artificial Intelligence ApplicationsMathematics, Computing, and Information Processing

Cited by 385

Mitigating Sybils in Federated Learning Poisoning

Fung, C., Yoon, C. J. M., & Beschastnikh, I. (2018) · arXiv

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 385

Emotional AI: The Rise of Empathic Media

McStay, A. (2018) · SAGE Publications Ltd eBooks

Innovative Human-Technology InteractionEthics and Social Impacts of AI

Cited by 385

Polynomial-time quantum algorithm for the simulation of chemical dynamics

Kassal, I., Jordan, S. P., Love, P. J., Mohseni, M., & Aspuru-Guzik, A. (2008) · Proceedings of the National Academy of Sciences

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 385

Learning to Quantize Deep Networks by Optimizing Quantization Intervals With Task Loss

Jung, S., Son, C., Lee, S., Son, J., Han, J.-J., Kwak, Y., Hwang, S. J., & Choi, C. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 385

Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks

Ding, J., Li, A., Hu, Z., & Wang, L. (2017) · Lecture Notes in Computer Science

AI in cancer detectionLung Cancer Diagnosis and Treatment

Cited by 385

The cognitive atlas: toward a knowledge foundation for cognitive neuroscience

Poldrack, R. A., Kittur, A., Kalar, D., Miller, E., Seppa, C., Gil, Y., Parker, D. S., Sabb, F. W., & Bilder, R. M. (2011) · Frontiers in Neuroinformatics

Cognitive Computing and NetworksNeural and Behavioral Psychology Studies

Cited by 385

Advances in wearable technology and applications in physical medicine and rehabilitation

Bonato, P. (2005) · Journal of NeuroEngineering and Rehabilitation

Context-Aware Activity Recognition SystemsNon-Invasive Vital Sign Monitoring

Cited by 385

Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation

Liu, J., Xia, C. S., Wang, Y., & Zhang, L. (2023) · Advances in Neural Information Processing Systems 36

Software Engineering ResearchSoftware Testing and Debugging Techniques

Cited by 384

Welcome to the Era of ChatGPT et al.: The Prospects of Large Language Models

Teubner, T., Flath, C. M., Weinhardt, C., van der Aalst, W., & Hinz, O. (2023) · Business & Information Systems Engineering

Artificial Intelligence ApplicationsTopic Modeling

Cited by 384