Check computer science papers: citations, claims and retractions

Intelligent Edge-Assisted Crowdcast with Deep Reinforcement Learning for Personalized QoE

Wang, F., Zhang, C., wang, F., Liu, J., Zhu, Y., Pang, H., & Sun, L. (2019) · IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

IoT and Edge/Fog ComputingImage and Video Quality Assessment

Cited by 87

What learning algorithm is in-context learning? Investigations with linear models

Akyürek, E., Schuurmans, D., Andreas, J., Ma, T., & Zhou, D. (2022) · arXiv

Domain Adaptation and Few-Shot LearningNeural Networks and Applications

Cited by 86

A Checklist to Combat Cognitive Biases in Crowdsourcing

Draws, T., Rieger, A., Inel, O., Gadiraju, U., & Tintarev, N. (2021) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing

Mobile Crowdsensing and CrowdsourcingComputational and Text Analysis Methods

Cited by 86

A Survey of Data Quality Requirements That Matter in ML Development Pipelines

Priestley, M., O’donnell, F., & Simperl, E. (2023) · Journal of Data and Information Quality

Data Quality and ManagementResearch Data Management Practices

Cited by 86

Delta Prime?

Hoover, D. L. (2004) · Literary and Linguistic Computing

Authorship Attribution and ProfilingDigital Humanities and Scholarship

Cited by 86

Model Evasion Attack on Intrusion Detection Systems using Adversarial Machine Learning

Ayub, M. A., Johnson, W. A., Talbert, D. A., & Siraj, A. (2020) · 2020 54th Annual Conference on Information Sciences and Systems (CISS)

Adversarial Robustness in Machine LearningNetwork Security and Intrusion Detection

Cited by 86

Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?

Gekhman, Z., Yona, G., Aharoni, R., Eyal, M., Feder, A., Reichart, R., & Herzig, J. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 86

FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU

Sheng, Y., Zheng, L., Yuan, B., Li, Z., Ryabinin, M., Fu, D. Y., Xie, Z., Chen, B., Barrett, C., Gonzalez, J. E., et al. (2023) · arXiv

Parallel Computing and Optimization TechniquesNatural Language Processing Techniques

Cited by 86

Efficient Human Activity Recognition Solving the Confusing Activities Via Deep Ensemble Learning

Zhu, R., Xiao, Z., Li, Y., Yang, M., Tan, Y., Zhou, L., Lin, S., & Wen, H. (2019) · IEEE Access

Context-Aware Activity Recognition SystemsGait Recognition and Analysis

Cited by 86

The Sanction of Authority: Promoting Public Trust in AI

Knowles, B., & Richards, J. T. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 86

Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation

Ma, R., Pang, G., Chen, L., & van den Hengel, A. (2022) · Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

Advanced Graph Neural NetworksAnomaly Detection Techniques and Applications

Cited by 86

14.4 A scalable speech recognizer with deep-neural-network acoustic models and voice-activated power gating

Price, M., Glass, J., & Chandrakasan, A. P. (2017) · 2017 IEEE International Solid-State Circuits Conference (ISSCC)

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 86

Fuzzing deep-learning libraries via automated relational API inference

Deng, Y., Yang, C., Wei, A., & Zhang, L. (2022) · Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Software Testing and Debugging TechniquesSoftware Engineering Research

Cited by 86

Analyzing Information Leakage of Updates to Natural Language Models

Zanella-Béguelin, S., Wutschitz, L., Tople, S., Rühle, V., Paverd, A., Ohrimenko, O., Köpf, B., & Brockschmidt, M. (2020) · Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 86

The Final Frontier: Deep Learning in Space

Kothari, V., Liberis, E., & Lane, N. D. (2020) · Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications

Advanced Neural Network ApplicationsSpace Technology and Applications

Cited by 86

Configuring global software teams: a multi-company analysis of project productivity, quality, and profits

Ramasubbu, N., Cataldo, M., Balan, R. K., & Herbsleb, J. D. (2011) · Proceedings of the 33rd International Conference on Software Engineering

Software Engineering Techniques and PracticesOutsourcing and Supply Chain Management

Cited by 86

Automatically Correcting Large Language Models: Surveying the Landscape of Diverse Automated Correction Strategies

Pan, L., Saxon, M., Xu, W., Nathani, D., Wang, X., & Wang, W. Y. (2024) · Transactions of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 86

Linguistically debatable or just plain wrong?

Plank, B., Hovy, D., & Søgaard, A. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Natural Language Processing TechniquesLinguistic Studies and Language Acquisition

Cited by 86

Affective states classification using EEG and semi-supervised deep learning approaches

Xu, H., & Plataniotis, K. N. (2016) · 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP)

Emotion and Mood RecognitionEEG and Brain-Computer Interfaces

Cited by 86

QoE-Driven Cross-Layer Optimization for Wireless Dynamic Adaptive Streaming of Scalable Videos Over HTTP

Zhao, M., Gong, X., Liang, J., Wang, W., Que, X., & Cheng, S. (2015) · IEEE Transactions on Circuits and Systems for Video Technology

Image and Video Quality AssessmentAdvanced Wireless Network Optimization

Cited by 86

Old is Gold: Linguistic Driven Approach for Entity and Relation Linking of Short Text

Sakor, A., Onando Mulang’, I., Singh, K., Shekarpour, S., Esther Vidal, M., Lehmann, J., & Auer, S. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 86

Benchmarking and scaling of deep learning models for land cover image classification

Papoutsis, I., Bountos, N. I., Zavras, A., Michail, D., & Tryfonopoulos, C. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing

Remote-Sensing Image ClassificationAdvanced Neural Network Applications

Cited by 86

Prevalence and predictors of data and code sharing in the medical and health sciences: systematic review with meta-analysis of individual participant data

Hamilton, D. G., Hong, K., Fraser, H., Rowhani-Farid, A., Fidler, F., & Page, M. J. (2023) · BMJ

Research Data Management PracticesAcademic Publishing and Open Access

Cited by 86

Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?

Gekhman, Z., Yona, G., Aharoni, R., Eyal, M., Feder, A., Reichart, R., & Herzig, J. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 86