Check computer science papers: citations, claims and retractions

Multimodal Multi-task Learning for Dimensional and Continuous Emotion Recognition

Chen, S., Jin, Q., Zhao, J., & Wang, S. (2017) · Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge

Emotion and Mood RecognitionSentiment Analysis and Opinion Mining

Cited by 147

Does Distance Still Matter? Revisiting the CSCW Fundamentals on Distributed Collaboration

Bjørn, P., Esbensen, M., Jensen, R. E., & Matthiesen, S. (2014) · ACM Transactions on Computer-Human Interaction

Software Engineering Techniques and PracticesTeam Dynamics and Performance

Cited by 147

Noise Injection: Theoretical Prospects

Grandvalet, Y., Canu, S., & Boucheron, S. (1997) · Neural Computation

Stochastic Gradient Optimization TechniquesNeural Networks and Applications

Cited by 147

IoU-Adaptive Deformable R-CNN: Make Full Use of IoU for Multi-Class Object Detection in Remote Sensing Imagery

Yan, J., Wang, H., Yan, M., Diao, W., Sun, X., & Li, H. (2019) · Remote Sensing

Advanced Neural Network ApplicationsRemote-Sensing Image Classification

Cited by 147

Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology

Dolezal, J. M., Srisuwananukorn, A., Karpeyev, D., Ramesh, S., Kochanny, S., Cody, B., Mansfield, A. S., Rakshit, S., Bansal, R., Bois, M. C., et al. (2022) · Nature Communications

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 147

On the depth of deep neural networks: a theoretical view

Sun, S., Chen, W., Wang, L., Liu, X., & Liu, T.-Y. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence

Neural Networks and ApplicationsStochastic Gradient Optimization Techniques

Cited by 147

Mixture class recovery in GMM under varying degrees of class separation: Frequentist versus Bayesian estimation.

Depaoli, S. (2013) · Psychological Methods

Bayesian Methods and Mixture ModelsStatistical Methods and Bayesian Inference

Cited by 147

Sensing Fine-Grained Hand Activity with Smartwatches

Laput, G., & Harrison, C. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

Context-Aware Activity Recognition SystemsHand Gesture Recognition Systems

Cited by 147

Cell Line Data Base: structure and recent improvements towards molecular authentication of human cell lines

Romano, P., Manniello, A., Aresu, O., Armento, M., Cesaro, M., & Parodi, B. (2009) · Nucleic Acids Research

Research Data Management PracticesBiomedical Text Mining and Ontologies

Cited by 147

Towards multimodal deep learning for activity recognition on mobile devices

Radu, V., Lane, N. D., Bhattacharya, S., Mascolo, C., Marina, M. K., & Kawsar, F. (2016) · Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct

Context-Aware Activity Recognition SystemsHuman Mobility and Location-Based Analysis

Cited by 147

NuClick: A deep learning framework for interactive segmentation of microscopic images

Alemi Koohbanani, N., Jahanifar, M., Zamani Tajadin, N., & Rajpoot, N. (2020) · Medical Image Analysis

AI in cancer detectionCell Image Analysis Techniques

Cited by 147

Algorithmic monoculture and social welfare

Kleinberg, J., & Raghavan, M. (2021) · Proceedings of the National Academy of Sciences

Ethics and Social Impacts of AIGame Theory and Voting Systems

Cited by 147

Utility is in the Eye of the User: A Critique of NLP Leaderboards

Ethayarajh, K., & Jurafsky, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 147

Exponential Error Suppression for Near-Term Quantum Devices

Koczor, B. (2021) · Physical Review X

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 147

Anomaly Detection for Insider Threats Using Unsupervised Ensembles

Le, D. C., & Zincir-Heywood, N. (2021) · IEEE Transactions on Network and Service Management

Network Security and Intrusion DetectionAnomaly Detection Techniques and Applications

Cited by 147

Interactive AI With Retrieval-Augmented Generation for Next Generation Networking

Zhang, R., Du, H., Liu, Y., Niyato, D., Kang, J., Sun, S., Shen, X., & Poor, H. V. (2024) · IEEE Network

Software-Defined Networks and 5GAdvanced Data and IoT Technologies

Cited by 147

A survey of resource constrained shortest path problems: Exact solution approaches

Pugliese, L. D. P., & Guerriero, F. (2013) · Networks

Complexity and Algorithms in GraphsResource-Constrained Project Scheduling

Cited by 147

Cybersecurity: Risks, vulnerabilities and countermeasures to prevent social engineering attacks

Conteh, N. Y., & Schmick, P. J. (2016) · International Journal of Advanced Computer Research

Information and Cyber SecuritySpam and Phishing Detection

Cited by 147

The rubber hand universe: On the impact of methodological differences in the rubber hand illusion

Riemer, M., Trojan, J., Beauchamp, M., & Fuchs, X. (2019) · Neuroscience & Biobehavioral Reviews

Virtual Reality Applications and ImpactsTactile and Sensory Interactions

Cited by 147

Activation Atlas

Carter, S., Armstrong, Z., Schubert, L., Johnson, I., & Olah, C. (2019) · Distill

Explainable Artificial Intelligence (XAI)Advanced Neural Network Applications

Cited by 147

Comparison of Six Cursor Control Devices Based on Fitts' Law Models

Epps, B. W. (1986) · Proceedings of the Human Factors Society Annual Meeting

Interactive and Immersive DisplaysMotor Control and Adaptation

Cited by 147

Neural Machine Translation with Byte-Level Subwords

Wang, C., Cho, K., & Gu, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence

Natural Language Processing TechniquesTopic Modeling

Cited by 147

Generative Multimodal Models are In-Context Learners

Sun, Q., Cui, Y., Zhang, X., Zhang, F., Yu, Q., Wang, Y., Rao, Y., Liu, J., Huang, T., & Wang, X. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 147

ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challenges

Kollias, D. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Emotion and Mood RecognitionFace recognition and analysis

Cited by 147