Check computer science papers: citations, claims and retractions

BDI agents in social simulations: a survey

Adam, C., & Gaudou, B. (2016) · The Knowledge Engineering Review

Multi-Agent Systems and NegotiationSimulation Techniques and Applications

Cited by 165

Data-efficient Fine-tuning for LLM-based Recommendation

Lin, X., Wang, W., Li, Y., Yang, S., Feng, F., Wei, Y., & Chua, T.-S. (2024) · arXiv

Recommender Systems and TechniquesTopic Modeling

Cited by 165

A Recurrent BERT-based Model for Question Generation

Chan, Y.-H., & Fan, Y.-C. (2019) · Proceedings of the 2nd Workshop on Machine Reading for Question Answering

Topic ModelingNatural Language Processing Techniques

Cited by 165

ADD 2022: the first Audio Deep Synthesis Detection Challenge

Yi, J., Fu, R., Tao, J., Nie, S., Ma, H., Wang, C., Wang, T., Tian, Z., Bai, Y., Fan, C., et al. (2022) · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 165

What Does it Mean for a Language Model to Preserve Privacy?

Brown, H., Lee, K., Mireshghallah, F., Shokri, R., & Tramèr, F. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency

Privacy-Preserving Technologies in DataEthics and Social Impacts of AI

Cited by 165

An information-theoretic approach to curiosity-driven reinforcement learning

Still, S., & Precup, D. (2012) · Theory in Biosciences

Reinforcement Learning in RoboticsAdvanced Bandit Algorithms Research

Cited by 165

I tried a bunch of things: The dangers of unexpected overfitting in classification of brain data

Hosseini, M., Powell, M., Collins, J., Callahan-Flintoft, C., Jones, W., Bowman, H., & Wyble, B. (2020) · Neuroscience & Biobehavioral Reviews

Machine Learning and Data ClassificationEEG and Brain-Computer Interfaces

Cited by 165

Nearest Neighbor Machine Translation

Khandelwal, U., Fan, A., Jurafsky, D., Zettlemoyer, L., & Lewis, M. (2020) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 165

A review on object detection in unmanned aerial vehicle surveillance

Ramachandran, A., & Sangaiah, A. K. (2021) · International Journal of Cognitive Computing in Engineering

Advanced Neural Network ApplicationsVideo Surveillance and Tracking Methods

Cited by 165

A basic agent

Vere, S., & Bickmore, T. (1990) · Computational Intelligence

AI-based Problem Solving and PlanningSpeech and dialogue systems

Cited by 165

Learning Invariant Representations for Reinforcement Learning without Reconstruction

Zhang, A., McAllister, R., Calandra, R., Gal, Y., & Levine, S. (2020) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 165

Managing Messes in Computational Notebooks

Head, A., Hohman, F., Barik, T., Drucker, S. M., & DeLine, R. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

Software Engineering ResearchScientific Computing and Data Management

Cited by 165

Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation

Jain, V., Magalhaes, G., Ku, A., Vaswani, A., Ie, E., & Baldridge, J. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Multimodal Machine Learning ApplicationsReinforcement Learning in Robotics

Cited by 165

VALIDATION OF "AW3D" GLOBAL DSM GENERATED FROM ALOS PRISM

Takaku, J., Tadono, T., Tsutsui, K., & Ichikawa, M. (2016) · ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences

Satellite Image Processing and Photogrammetry3D Surveying and Cultural Heritage

Cited by 165

A survey on aspect-based sentiment classification

Brauwers, G., & Frasincar, F. (2023) · ACM Computing Surveys

Sentiment Analysis and Opinion MiningTopic Modeling

Cited by 165

LUVLi Face Alignment: Estimating Landmarks’ Location, Uncertainty, and Visibility Likelihood

Kumar, A., Marks, T. K., Mou, W., Wang, Y., Jones, M., Cherian, A., Koike-Akino, T., Liu, X., & Feng, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Face recognition and analysisFace Recognition and Perception

Cited by 165

Designing for Responsible Trust in AI Systems: A Communication Perspective

Liao, Q. V., & Sundar, S. S. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 165

A Probabilistic Model for Using Social Networks in Personalized Item Recommendation

Chaney, A. J. B., Blei, D. M., & Eliassi-Rad, T. (2015) · Proceedings of the 9th ACM Conference on Recommender Systems

Recommender Systems and TechniquesAdvanced Bandit Algorithms Research

Cited by 164

Machine Unlearning: A Survey

Xu, H., Zhu, T., Zhang, L., Zhou, W., & Yu, P. S. (2024) · ACM Computing Surveys

Privacy-Preserving Technologies in DataCryptography and Data Security

Cited by 164

Recognizing and utilizing novel research opportunities with artificial intelligence

von Krogh, G., Roberson, Q., & Gruber, M. (2023) · Academy of Management Journal

Artificial Intelligence ApplicationsImpact of AI and Big Data on Business and Society

Cited by 164

Quantum orbital-optimized unitary coupled cluster methods in the strongly correlated regime: Can quantum algorithms outperform their classical equivalents?

Sokolov, I. O., Barkoutsos, P. K., Ollitrault, P. J., Greenberg, D., Rice, J., Pistoia, M., & Tavernelli, I. (2020) · The Journal of Chemical Physics

Quantum Computing Algorithms and ArchitectureAdvanced Chemical Physics Studies

Cited by 164

A tool to assess the comfort of wearable computers

Knight, J. F., & Baber, C. (2005) · Human Factors The Journal of the Human Factors and Ergonomics Society

Innovative Human-Technology InteractionUsability and User Interface Design

Cited by 164

A comprehensive survey of numeric and symbolic outlier mining techniques

Agyemang, M., Barker, K., & Alhajj, R. (2006) · Intelligent Data Analysis

Anomaly Detection Techniques and ApplicationsAdvanced Statistical Methods and Models

Cited by 164

Examining EFL teachers’ technological pedagogical content knowledge and the adoption of mobile-assisted language learning: A partial least square approach

Hsu, L. (2016) · Computer Assisted Language Learning

Mobile Learning in EducationTechnology Adoption and User Behaviour

Cited by 164