Check computer science papers: citations, claims and retractions

Sequence-Level Knowledge Distillation

Kim, Y., & Rush, A. M. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

Natural Language Processing TechniquesAdvanced Neural Network Applications

Cited by 836

Understanding how image quality affects deep neural networks

Dodge, S., & Karam, L. (2016) · 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX)

Advanced Neural Network ApplicationsImage and Video Quality Assessment

Cited by 835

ElasticFusion: Dense SLAM Without A Pose Graph

Whelan, T., Leutenegger, S., Salas Moreno, R., Glocker, B., & Davison, A. (2015) · Robotics: Science and Systems XI

Robotics and Sensor-Based Localization3D Surveying and Cultural Heritage

Cited by 835

PaLM 2 Technical Report

Anil, R., Dai, A. M., Firat, O., Johnson, M., Lepikhin, D., Passos, A., Shakeri, S., Taropa, E., Bailey, P., Chen, Z., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 834

Automated machine learning: Review of the state-of-the-art and opportunities for healthcare

Waring, J., Lindvall, C., & Umeton, R. (2020) · Artificial Intelligence in Medicine

Machine Learning and Data ClassificationArtificial Intelligence in Healthcare and Education

Cited by 834

The devil is in the details: an evaluation of recent feature encoding methods

Chatfield, K., Lempitsky, V., Vedaldi, A., & Zisserman, A. (2011) · Procedings of the British Machine Vision Conference 2011

Advanced Image and Video Retrieval TechniquesImage Retrieval and Classification Techniques

Cited by 834

Catastrophic forgetting, rehearsal and pseudorehearsal

ROBINS, A. (1995) · Connection Science

Domain Adaptation and Few-Shot LearningMemory Processes and Influences

Cited by 833

Real-Time Computation at the Edge of Chaos in Recurrent Neural Networks

Bertschinger, N., & Natschläger, T. (2004) · Neural Computation

Neural Networks and ApplicationsCellular Automata and Applications

Cited by 833

AttGAN: Facial attribute editing by only changing what you want

He, Z., Zuo, W., Kan, M., Shan, S., & Chen, X. (2019) · IEEE Transactions on Image Processing

Generative Adversarial Networks and Image SynthesisFace recognition and analysis

Cited by 833

CPAR: Classification based on Predictive Association Rules

Yin, X., & Han, J. (2003) · Proceedings of the 2003 SIAM International Conference on Data Mining

Data Mining Algorithms and ApplicationsData Mining and Machine Learning Applications

Cited by 833

Information Seeking in Electronic Environments

Marchionini, G. (1995) · Cambridge University Press eBooks

Information Retrieval and Search BehaviorLibrary Science and Information Literacy

Cited by 833

Interpretable Convolutional Neural Networks

Zhang, Q., Wu, Y. N., & Zhu, S.-C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Explainable Artificial Intelligence (XAI)Advanced Neural Network Applications

Cited by 833

Reinforcement learning of motor skills with policy gradients

Peters, J., & Schaal, S. (2008) · Neural Networks

Reinforcement Learning in RoboticsMotor Control and Adaptation

Cited by 831

HellaSwag: Can a Machine Really Finish Your Sentence?

Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., & Choi, Y. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 830

Incentives and Problem Uncertainty in Innovation Contests: An Empirical Analysis

Boudreau, K. J., Lacetera, N., & Lakhani, K. R. (2011) · Management Science

Open Source Software InnovationsAuction Theory and Applications

Cited by 830

Chatbots for language learning—Are they really useful? A systematic review of chatbot‐supported language learning

Huang, W., Hew, K. F., & Fryer, L. K. (2022) · Journal of Computer Assisted Learning

Intelligent Tutoring Systems and Adaptive LearningAI in Service Interactions

Cited by 830

How to factor 2048 bit RSA integers in 8 hours using 20 million noisy qubits

Gidney, C., & Ekerå, M. (2021) · Quantum

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 830

Speeding Up Distributed Machine Learning Using Codes

Lee, K., Lam, M., Pedarsani, R., Papailiopoulos, D., & Ramchandran, K. (2018) · IEEE Transactions on Information Theory

Advanced Data Storage TechnologiesStochastic Gradient Optimization Techniques

Cited by 829

Fog Computing: Platform and Applications

Yi, S., Hao, Z., Qin, Z., & Li, Q. (2015) · 2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb)

IoT and Edge/Fog ComputingCloud Computing and Resource Management

Cited by 829

Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints

Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Ethics and Social Impacts of AIAdvanced Neural Network Applications

Cited by 829

WikiQA: A Challenge Dataset for Open-Domain Question Answering

Yang, Y., Yih, W.-T., & Meek, C. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

Topic ModelingInformation Retrieval and Search Behavior

Cited by 829

Edge artificial intelligence for 6g: vision, enabling technologies, and applications

Letaief, K. B., Shi, Y., Lu, J., & Lu, J. (2022) · IEEE Journal on Selected Areas in Communications

IoT and Edge/Fog ComputingAdvanced Wireless Communication Technologies

Cited by 829

Self-Consistency Improves Chain of Thought Reasoning in Language Models

Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., & Zhou, D. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 829

Local Model Poisoning Attacks to Byzantine-Robust Federated Learning

Fang, M., Cao, X., Jia, J., & Gong, N. Z. (2019) · arXiv

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 828