Check computer science papers: citations, claims and retractions

Knowledge Distillation with the Reused Teacher Classifier

Chen, D., Mei, J.-P., Zhang, H., Wang, C., Feng, Y., & Chen, C. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 254

Qubit allocation

Siraichi, M. Y., Santos, V. F. D., Collange, C., & Pereira, F. M. Q. (2018) · Proceedings of the 2018 International Symposium on Code Generation and Optimization

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 254

Neural Text Generation with Unlikelihood Training

Welleck, S., Kulikov, I., Roller, S., Dinan, E., Cho, K., & Weston, J. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 254

Persistent homology analysis of protein structure, flexibility, and folding

Xia, K., & Wei, G. (2014) · International Journal for Numerical Methods in Biomedical Engineering

Topological and Geometric Data AnalysisProtein Structure and Dynamics

Cited by 254

An intuitive framework for real-time freeform modeling

Botsch, M., & Kobbelt, L. (2004) · ACM Transactions on Graphics

3D Shape Modeling and AnalysisComputer Graphics and Visualization Techniques

Cited by 254

How WEIRD is CHI?

Linxen, S., Sturm, C., Brühlmann, F., Cassau, V., Opwis, K., & Reinecke, K. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

Usability and User Interface DesignInnovative Human-Technology Interaction

Cited by 254

Dynabench: Rethinking Benchmarking in NLP

Kiela, D., Bartolo, M., Nie, Y., Kaushik, D., Geiger, A., Wu, Z., Vidgen, B., Prasad, G., Singh, A., Ringshia, P., et al. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingNatural Language Processing Techniques

Cited by 254

Clinical applications of continual learning machine learning

Lee, C. S., & Lee, A. Y. (2020) · The Lancet Digital Health

Domain Adaptation and Few-Shot LearningArtificial Intelligence in Healthcare and Education

Cited by 254

Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?

Hase, P., & Bansal, M. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 254

How Much Position Information Do Convolutional Neural Networks Encode?

Islam, M. A., Jia, S., & Bruce, N. D. B. (2020) · arXiv

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 253

Knowledge Representation, Reasoning, and the Design of Intelligent Agents: The Answer-Set Programming Approach

Gelfond, M., & Kahl, Y. (2014) · Cambridge University Press eBooks

Logic, Reasoning, and KnowledgeAI-based Problem Solving and Planning

Cited by 253

An Analysis of Incorporating an External Language Model into a Sequence-to-Sequence Model

Kannan, A., Wu, Y., Nguyen, P., Sainath, T. N., Chen, Z., & Prabhavalkar, R. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 253

MemoryBank: Enhancing Large Language Models with Long-Term Memory

Zhong, W., Guo, L., Gao, Q., Ye, H., & Wang, Y. (2023) · arXiv

Speech and dialogue systemsTopic Modeling

Cited by 253

A Survey on Deepfake Video Detection

Yu, P., Xia, Z., Fei, J., & Lu, Y. (2021) · IET Biometrics

Digital Media Forensic DetectionGenerative Adversarial Networks and Image Synthesis

Cited by 253

Scalarized multi-objective reinforcement learning: Novel design techniques

Van Moffaert, K., Drugan, M. M., & Nowe, A. (2013) · 2013 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)

Advanced Multi-Objective Optimization AlgorithmsReinforcement Learning in Robotics

Cited by 253

Software Mitigation of Crosstalk on Noisy Intermediate-Scale Quantum Computers

Murali, P., Mckay, D. C., Martonosi, M., & Javadi-Abhari, A. (2020) · Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 253

Applications of augmented reality-based natural interactive learning in magnetic field instruction

Cai, S., Chiang, F.-K., Sun, Y., Lin, C., & Lee, J. J. (2017) · Interactive Learning Environments

Augmented Reality ApplicationsMultimedia Learning Systems

Cited by 253

Reading text from computer screens

Mills, C. B., & Weldon, L. J. (1987) · ACM Computing Surveys

Usability and User Interface DesignGaze Tracking and Assistive Technology

Cited by 253

On Time Versus Space

Hopcroft, J., Paul, W., & Valiant, L. (1977) · Journal of the ACM

semigroups and automata theoryComputability, Logic, AI Algorithms

Cited by 253

Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems

Lei, W., He, X., Miao, Y., Wu, Q., Hong, R., Kan, M.-Y., & Chua, T.-S. (2020) · Proceedings of the 13th International Conference on Web Search and Data Mining

Recommender Systems and TechniquesInformation Retrieval and Search Behavior

Cited by 253

Putting AI ethics to work: are the tools fit for purpose?

Ayling, J., & Chapman, A. (2022) · AI and Ethics

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 253

Compressing Context to Enhance Inference Efficiency of Large Language Models

Li, Y., Dong, B., Guerin, F., & Lin, C. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 253

Towards Out-Of-Distribution Generalization: A Survey

Liu, J., Shen, Z., He, Y., Zhang, X., Xu, R., Yu, H., & Cui, P. (2021) · arXiv

Domain Adaptation and Few-Shot LearningAdversarial Robustness in Machine Learning

Cited by 253

Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection

Han, C., Rundo, L., Araki, R., Nagano, Y., Furukawa, Y., Mauri, G., Nakayama, H., & Hayashi, H. (2019) · IEEE Access

Brain Tumor Detection and ClassificationGenerative Adversarial Networks and Image Synthesis

Cited by 253