Check computer science papers: citations, claims and retractions

A survey of statistical user simulation techniques for reinforcement-learning of dialogue management strategies

SCHATZMANN, J., WEILHAMMER, K., STUTTLE, M., & YOUNG, S. (2006) · The Knowledge Engineering Review

Speech and dialogue systemsIntelligent Tutoring Systems and Adaptive Learning

Cited by 336

Robust hash functions for digital watermarking

Fridrich, J., & Goljan, M. (2002) · Proceedings International Conference on Information Technology: Coding and Computing (Cat. No.PR00540)

Advanced Steganography and Watermarking TechniquesComputer Science and Engineering

Cited by 336

AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss

Qian, K., Zhang, Y., Chang, S., Yang, X., & Hasegawa-Johnson, M. (2019) · arXiv

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 336

Community‐Based Innovation Contests: Where Competition Meets Cooperation

Bullinger, A. C., Neyer, A., Rass, M., & Moeslein, K. M. (2010) · Creativity and Innovation Management

Open Source Software InnovationsBiomedical and Engineering Education

Cited by 336

Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records

Thorsen-Meyer, H.-C., Nielsen, A. B., Nielsen, A. P., Kaas-Hansen, B. S., Toft, P., Schierbeck, J., Strøm, T., Chmura, P. J., Heimann, M., Dybdahl, L., et al. (2020) · The Lancet Digital Health

Machine Learning in HealthcareSepsis Diagnosis and Treatment

Cited by 336

DeepFL: integrating multiple fault diagnosis dimensions for deep fault localization

Li, X., Li, W., Zhang, Y., & Zhang, L. (2019) · Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis

Software Testing and Debugging TechniquesSoftware Engineering Research

Cited by 336

Anthropomorphism in AI

Salles, A., Evers, K., & Farisco, M. (2020) · AJOB Neuroscience

Ethics and Social Impacts of AICybernetics and Technology in Society

Cited by 335

Low-Depth Quantum Simulation of Materials

Babbush, R., Wiebe, N., McClean, J., McClain, J., Neven, H., & Chan, G. K.-L. (2018) · Physical Review X

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 335

Socially translucent systems: social proxies, persistent conversation, and the design of “babble”

Erickson, T., Smith, D. N., Kellogg, W. A., Laff, M., Richards, J. T., & Bradner, E. (1999) · Proceedings of the SIGCHI conference on Human factors in computing systems the CHI is the limit - CHI '99

Usability and User Interface DesignInnovative Human-Technology Interaction

Cited by 335

The citation advantage of linking publications to research data

Colavizza, G., Hrynaszkiewicz, I., Staden, I., Whitaker, K., & McGillivray, B. (2020) · PLOS ONE

Research Data Management Practicesscientometrics and bibliometrics research

Cited by 335

How AI revolutionizes innovation management – Perceptions and implementation preferences of AI-based innovators

Füller, J., Hutter, K., Wahl, J., Bilgram, V., & Tekic, Z. (2022) · Technological Forecasting and Social Change

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

Cited by 335

Technology readiness and the organizational journey towards AI adoption: An empirical study

Uren, V., & Edwards, J. S. (2023) · International Journal of Information Management

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

Cited by 335

Cross-Layer Distillation with Semantic Calibration

Chen, D., Mei, J.-P., Zhang, Y., Wang, C., Wang, Z., Feng, Y., & Chen, C. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 335

Adaptive Computation Time for Recurrent Neural Networks

Graves, A. (2016) · arXiv

Neural Networks and ApplicationsTopic Modeling

Cited by 335

SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability

Raghu, M., Gilmer, J., Yosinski, J., & Sohl-Dickstein, J. (2017) · arXiv

Explainable Artificial Intelligence (XAI)Neural Networks and Applications

Cited by 335

A generic framework for privacy preserving deep learning

Ryffel, T., Trask, A., Dahl, M., Wagner, B., Mancuso, J., Rueckert, D., & Passerat-Palmbach, J. (2018) · arXiv

Privacy-Preserving Technologies in DataCryptography and Data Security

Cited by 335

A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data

Khushi, M., Shaukat, K., Alam, T. M., Hameed, I. A., Uddin, S., Luo, S., Yang, X., & Reyes, M. C. (2021) · IEEE Access

Imbalanced Data Classification TechniquesMachine Learning and Data Classification

Cited by 335

Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers

Li, Z., Liu, X., Drenkow, N., Ding, A., Creighton, F. X., Taylor, R. H., & Unberath, M. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Advanced Vision and ImagingAdvanced Neural Network Applications

Cited by 335

UNPU: An Energy-Efficient Deep Neural Network Accelerator With Fully Variable Weight Bit Precision

Lee, J., Kim, C., Kang, S., Shin, D., Kim, S., & Yoo, H.-J. (2019) · IEEE Journal of Solid-State Circuits

Advanced Neural Network ApplicationsLow-power high-performance VLSI design

Cited by 335

Simple unification-based type inference for GADTs

Peyton Jones, S., Vytiniotis, D., Weirich, S., & Washburn, G. (2006) · Proceedings of the eleventh ACM SIGPLAN international conference on Functional programming

Logic, programming, and type systemsAdvanced Database Systems and Queries

Cited by 335

Feature Selection and Activity Recognition System Using a Single Triaxial Accelerometer

Gupta, P., & Dallas, T. (2014) · IEEE Transactions on Biomedical Engineering

Context-Aware Activity Recognition SystemsGait Recognition and Analysis

Cited by 335

Neural Text Summarization: A Critical Evaluation

Kryscinski, W., Keskar, N. S., McCann, B., Xiong, C., & Socher, R. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Topic ModelingAdvanced Text Analysis Techniques

Cited by 335

A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4

Kalyan, K. S. (2024) · Natural Language Processing Journal

Topic ModelingNatural Language Processing Techniques

Cited by 335

Detecting mammals in UAV images: best practices to address a substantially imbalanced dataset with deep learning

Kellenberger, B., Marcos, D., & Tuia, D. (2018) · Remote Sensing of Environment

Advanced Neural Network ApplicationsWildlife Ecology and Conservation

Cited by 335