Check computer science papers: citations, claims and retractions

MemFlow: Optical Flow Estimation and Prediction with Memory

Dong, Q., & Fu, Y. (2024) · arXiv

Advanced Vision and ImagingVideo Surveillance and Tracking Methods

Cited by 49

Classification of animal sounds in a hyperdiverse rainforest using convolutional neural networks with data augmentation

Sun, Y., Midori Maeda, T., Solís-Lemus, C., Pimentel-Alarcón, D., & Buřivalová, Z. (2022) · Ecological Indicators

Music and Audio ProcessingAnimal Vocal Communication and Behavior

Cited by 49

MuLan: A Joint Embedding of Music Audio and Natural Language

Huang, Q., Jansen, A., Lee, J., Ganti, R., Li, J. Y., & Ellis, D. P. W. (2022) · arXiv

Music and Audio ProcessingMusic Technology and Sound Studies

Cited by 49

Pedestrian Target Tracking Based On DeepSORT With YOLOv5

Gai, Y., He, W., & Zhou, Z. (2021) · 2021 2nd International Conference on Computer Engineering and Intelligent Control (ICCEIC)

Video Surveillance and Tracking MethodsAutonomous Vehicle Technology and Safety

Cited by 49

Coordinated Joint Multimodal Embeddings for Generalized Audio-Visual Zero-shot Classification and Retrieval of Videos

Parida, K. K., Matiyali, N., Guha, T., & Sharma, G. (2020) · 2020 IEEE Winter Conference on Applications of Computer Vision (WACV)

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 49

Parameter Efficient Multimodal Transformers for Video Representation Learning

Lee, S., Yu, Y., Kim, G., Breuel, T., Kautz, J., & Song, Y. (2020) · arXiv

Multimodal Machine Learning ApplicationsHuman Pose and Action Recognition

Cited by 49

Generation of Synthetic Electronic Medical Record Text

Guan, J., Li, R., Yu, S., & Zhang, X. (2018) · 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 49

ARBR: Adaptive reinforcement-based routing for DTN

Elwhishi, A., Ho, P.-H., Naik, K., & Shihada, B. (2010) · 2010 IEEE 6th International Conference on Wireless and Mobile Computing, Networking and Communications

Opportunistic and Delay-Tolerant NetworksMobile Ad Hoc Networks

Cited by 49

Using software trails to reconstruct the evolution of software

German, D. M. (2004) · Journal of Software Maintenance and Evolution: Research and Practice

Software Engineering ResearchOpen Source Software Innovations

Cited by 49

MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning

Karpas, E., Abend, O., Belinkov, Y., Lenz, B., Lieber, O., Ratner, N., Shoham, Y., Bata, H., Levine, Y., Leyton-Brown, K., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 49

Large Language Models as General Pattern Machines

Mirchandani, S., Xia, F., Florence, P., Ichter, B., Driess, D., Arenas, M. G., Rao, K., Sadigh, D., & Zeng, A. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 49

When Is an Algorithm Transparent? Predictive Analytics, Privacy, and Public Policy

Sloan, R. H., & Warner, R. (2018) · IEEE Security & Privacy

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 49

“Happiness Inventors”: Informing Positive Computing Technologies Through Participatory Design With Children

Yarosh, S., & Schueller, S. M. (2017) · Journal of Medical Internet Research

Innovative Human-Technology InteractionDigital Mental Health Interventions

Cited by 49

Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance

Jean, N., Xie, S. M., & Ermon, S. (2018) · arXiv

Gaussian Processes and Bayesian InferenceMachine Learning and Algorithms

Cited by 49

Racial, skin tone, and sex disparities in automated proctoring software

Yoder-Himes, D. R., Asif, A., Kinney, K., Brandt, T. J., Cecil, R. E., Himes, P. R., Cashon, C., Hopp, R. M. P., & Ross, E. (2022) · Frontiers in Education

Ethics and Social Impacts of AIAcademic integrity and plagiarism

Cited by 49

Robust people counting system based on sensor fusion

Dan, B.-K., Kim, Y.-S., Suryanto, Jung, J.-Y., & Ko, S.-J. (2012) · IEEE Transactions on Consumer Electronics

Video Surveillance and Tracking MethodsFace recognition and analysis

Cited by 49

Security in Multi-Agent Systems

Hedin, Y., & Moradian, E. (2015) · Procedia Computer Science

Access Control and TrustMulti-Agent Systems and Negotiation

Cited by 49

Implementation and Tuning of Batched Cholesky Factorization and Solve for NVIDIA GPUs

Kurzak, J., Anzt, H., Gates, M., & Dongarra, J. (2016) · IEEE Transactions on Parallel and Distributed Systems

Parallel Computing and Optimization TechniquesMatrix Theory and Algorithms

Cited by 49

Continuous-Time Trajectory Estimation for Event-based Vision Sensors

Mueggler, E., Gallego, G., & Scaramuzza, D. (2015) · Robotics: Science and Systems XI

Robotics and Sensor-Based LocalizationAdvanced Vision and Imaging

Cited by 49

Large Language Model-Based Agents for Software Engineering: A Survey

Liu, J., Wang, K., Chen, Y., Peng, X., Chen, Z., Zhang, L., & Lou, Y. (2024) · arXiv

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 49

DLA: Detecting and Localizing Anomalies in Containerized Microservice Architectures Using Markov Models

Samir, A., & Pahl, C. (2019) · 2019 7th International Conference on Future Internet of Things and Cloud (FiCloud)

Software System Performance and ReliabilityCloud Computing and Resource Management

Cited by 49

eHealth integrity model based on permissioned blockchain

Hyla, T., & Pejaś, J. (2019) · Future Internet

Blockchain Technology Applications and SecurityBlockchain Technology in Education and Learning

Cited by 49

MAGMA – Multimodal Augmentation of Generative Models through Adapter-based Finetuning

Eichenberg, C., Black, S., Weinbach, S., Parcalabescu, L., & Frank, A. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 49

Can Neural Machine Translation be Improved with User Feedback?

Kreutzer, J., Khadivi, S., Matusov, E., & Riezler, S. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)

Natural Language Processing TechniquesSpeech Recognition and Synthesis

Cited by 49