Check computer science papers: citations, claims and retractions

Deep Learning: A Bayesian Perspective

Polson, N. G., & Sokolov, V. (2017) · Bayesian Analysis

Gaussian Processes and Bayesian InferenceNeural Networks and Applications

Cited by 120

Human-Centered Explainable AI (XAI): From Algorithms to User Experiences

Liao, Q. V., & Varshney, K. R. (2021) · arXiv

Explainable Artificial Intelligence (XAI)Usability and User Interface Design

Cited by 120

An Appraisal of Incremental Learning Methods

Luo, Y., Yin, L., Bai, W., & Mao, K. (2020) · Entropy

Domain Adaptation and Few-Shot LearningData Stream Mining Techniques

Cited by 120

Machine-Learning-Based Darknet Traffic Detection System for IoT Applications

Abu Al-Haija, Q., Krichen, M., & Abu Elhaija, W. (2022) · Electronics

Internet Traffic Analysis and Secure E-votingNetwork Security and Intrusion Detection

Cited by 120

Task-Oriented Query Reformulation with Reinforcement Learning

Nogueira, R., & Cho, K. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Information Retrieval and Search BehaviorRecommender Systems and Techniques

Cited by 120

Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces

Yang, J., Yang, S., Gupta, A. W., Han, R., Fei-Fei, L., & Xie, S. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsSpatial Cognition and Navigation

Cited by 120

Gaussian boson sampling using threshold detectors

Quesada, N., Arrazola, J. M., & Killoran, N. (2018) · Physical Review A

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 120

Authorship Attribution for Neural Text Generation

Uchendu, A., Le, T., Shu, K., & Lee, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Authorship Attribution and ProfilingTopic Modeling

Cited by 120

AtP*: An efficient and scalable method for localizing LLM behaviour to components

Kramár, J., Lieberum, T., Shah, R., & Nanda, N. (2024) · arXiv

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 120

How innovation commons contribute to discovering and developing new technologies

Allen, D. W. E., & Potts, J. (2016) · International Journal of the Commons

Open Source Software InnovationsEconomic Theory and Institutions

Cited by 120

Evidence of scaling advantage for the quantum approximate optimization algorithm on a classically intractable problem

Shaydulin, R., Li, C., Chakrabarti, S., DeCross, M., Herman, D., Kumar, N., Larson, J., Lykov, D., Minssen, P., Sun, Y., et al. (2024) · Science Advances

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 120

On the Self-Verification Limitations of Large Language Models on Reasoning and Planning Tasks

Stechly, K., Valmeekam, K., & Kambhampati, S. (2024) · arXiv

Topic ModelingAI-based Problem Solving and Planning

Cited by 120

A Privacy-Preserving Optimization of Neighborhood-Based Recommendation for Medical-Aided Diagnosis and Treatment

Zhang, M., Chen, Y., & Lin, J. (2021) · IEEE Internet of Things Journal

Privacy-Preserving Technologies in DataCryptography and Data Security

Cited by 120

Virtual Reality for the Enhancement of Emotion Regulation

Colombo, D., Díaz‐García, A., Fernandez‐Álvarez, J., & Botella, C. (2021) · Clinical Psychology & Psychotherapy

Virtual Reality Applications and ImpactsAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes

Cited by 120

Recommendation for key management, part 1: general (revision 3)

Barker, E. B., Barker, W. C., Burr, W. E., Polk, W. T., & Smid, M. E. (2012)

Cryptographic Implementations and SecurityCryptography and Data Security

Cited by 120

The limitations and ethical considerations of ChatGPT

Hua, S., Jin, S., & Jiang, S. (2024) · Data Intelligence

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 120

Augmented Reality and Virtual Reality in Education: Public Perspectives, Sentiments, Attitudes, and Discourses

Lampropoulos, G., Keramopoulos, E., Diamantaras, K., & Evangelidis, G. (2022) · Education Sciences

Augmented Reality ApplicationsSentiment Analysis and Opinion Mining

Cited by 119

QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization

Fabbri, A., Wu, C.-S., Liu, W., & Xiong, C. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingAdvanced Text Analysis Techniques

Cited by 119

Enhancing Critical Thinking in Education by means of a Socratic Chatbot

Favero, L., Pérez-Ortiz, J. A., Käser, T., & Oliver, N. (2024) · arXiv

Intelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in Education

Cited by 119

“What It Wants Me To Say”: Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models

Liu, M. X., Sarkar, A., Negreanu, C., Zorn, B., Williams, J., Toronto, N., & Gordon, A. D. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

Spreadsheets and End-User ComputingSoftware Engineering Research

Cited by 119

Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models

Levy, M., Jacoby, A., & Goldberg, Y. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 119

FocalFormer3D : Focusing on Hard Instance for 3D Object Detection

Chen, Y., Yu, Z., Chen, Y., Lan, S., Anandkumar, A., Jia, J., & Alvarez, J. M. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsAutonomous Vehicle Technology and Safety

Cited by 119

The Troubling Emergence of Hallucination in Large Language Models - An Extensive Definition, Quantification, and Prescriptive Remediations

Rawte, V., Chakraborty, S., Pathak, A., Sarkar, A., Tonmoy, S. M. T. I., Chadha, A., Sheth, A., & Das, A. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 119

Low-depth circuit ansatz for preparing correlated fermionic states on a quantum computer

Dallaire-Demers, P.-L., Romero, J., Veis, L., Sim, S., & Aspuru-Guzik, A. (2019) · Quantum Science and Technology

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 119