Check computer science papers: citations, claims and retractions

The Learnability of In-Context Learning

Wies, N., Levine, Y., & Shashua, A. (2023) · Advances in Neural Information Processing Systems 36

Domain Adaptation and Few-Shot LearningMachine Learning and Algorithms

Cited by 36

ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles

Raji, I. D., & Yang, J. (2019) · arXiv

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 36

From Sparse to Soft Mixtures of Experts

Puigcerver, J., Riquelme, C., Mustafa, B., & Houlsby, N. (2023) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 36

Adiabatic optimization versus diffusion Monte Carlo methods

Jarret, M., Jordan, S. P., & Lackey, B. (2016) · Physical Review A

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 36

Representer Theorems for Sparsity-Promoting $\ell _{1}$ Regularization

Unser, M., Fageot, J., & Gupta, H. (2016) · IEEE Transactions on Information Theory

Sparse and Compressive Sensing TechniquesNumerical methods in inverse problems

Cited by 36

iPFlakies: a framework for detecting and fixing python order-dependent flaky tests

Wang, R., Chen, Y., & Lam, W. (2022) · Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings

Software Testing and Debugging TechniquesSoftware Engineering Research

Cited by 36

A Definition of Continual Reinforcement Learning

Abel, D., Barreto, A., Roy, B. V., Precup, D., Hasselt, H. V., & Singh, S. (2023) · Advances in Neural Information Processing Systems 36

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 36

Large Language Models Can Self-Correct with Key Condition Verification

Wu, Z., Zeng, Q., Zhang, Z., Tan, Z., Shen, C., & Jiang, M. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 36

Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpus

Bandy, J., & Vincent, N. (2021) · arXiv

Ethics and Social Impacts of AIResearch Data Management Practices

Cited by 36

Learning algorithms and discrimination

Packin, N. G., & Lev-Aretz, Y. (2018) · Research Handbook on the Law of Artificial Intelligence

Ethics and Social Impacts of AIDigitalization, Law, and Regulation

Cited by 36

Learning Disentangled Representation for Cross-Modal Retrieval with Deep Mutual Information Estimation

Guo, W., Huang, H., Kong, X., & He, R. (2019) · Proceedings of the 27th ACM International Conference on Multimedia

Multimodal Machine Learning ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 36

Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking

Zhuang, S., Liu, B., Koopman, B., & Zuccon, G. (2023) · arXiv

Information Retrieval and Search BehaviorTopic Modeling

Cited by 36

Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation

Ning, X., Lin, Z., Zhou, Z., Wang, Z., Yang, H., & Wang, Y. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 36

Applying large language models for automated essay scoring for non-native Japanese

Li, W., & Liu, H. (2024) · Humanities and Social Sciences Communications

Natural Language Processing TechniquesEFL/ESL Teaching and Learning

Cited by 36

Adaptive Wavenet Vocoder for Residual Compensation in GAN-Based Voice Conversion

Sisman, B., Zhang, M., Sakti, S., Li, H., & Nakamura, S. (2018) · 2018 IEEE Spoken Language Technology Workshop (SLT)

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 36

RefSum: Refactoring Neural Summarization

Liu, Y., Dou, Z.-Y., & Liu, P. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingAdvanced Text Analysis Techniques

Cited by 36

Deep Learning Based Affective Model for Speech Emotion Recognition

Zhou, X., Guo, J., & Bie, R. (2016) · 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld)

Emotion and Mood RecognitionSpeech Recognition and Synthesis

Cited by 36

TinyML for Ubiquitous Edge AI

Soro, S. (2021) · arXiv

IoT and Edge/Fog ComputingAdvanced Neural Network Applications

Cited by 36

AI-Powered Health Chatbots: Toward a general architecture

Khadija, A., Zahra, F. F., & Naceur, A. (2021) · Procedia Computer Science

Speech and dialogue systemsArtificial Intelligence in Healthcare and Education

Cited by 36

Generating Mobile Application Onboarding Insights Through Minimalist Instruction

Strahm, B., Gray, C. M., & Vorvoreanu, M. (2018) · Proceedings of the 2018 Designing Interactive Systems Conference

Usability and User Interface DesignInnovative Human-Technology Interaction

Cited by 36

The AI teacher test: Measuring the pedagogical ability of blender and GPT-3 in educational dialogues

Tack, A., & Piech, C. (2022) · arXiv

Intelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in Education

Cited by 36

Task Scheduling with Optimized Transmission Time in Collaborative Cloud-Edge Learning

Huang, Y., Zhu, Y., Fan, X., Ma, X., Wang, F., Liu, J., Wang, Z., & Cui, Y. (2018) · 2018 27th International Conference on Computer Communication and Networks (ICCCN)

IoT and Edge/Fog ComputingCloud Computing and Resource Management

Cited by 36

Humanity's Last Exam

Phan, L., Gatti, A., Han, Z., & Li, N. (2025) · SuperIntelligence - Robotics - Safety & Alignment

Topic ModelingMultimodal Machine Learning Applications

Cited by 36

Toward Trainability of Quantum Neural Networks

Zhang, K., Hsieh, M.-H., Liu, L., & Tao, D. (2020) · arXiv

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 36