Check computer science papers: citations, claims and retractions

Detection of Novel Social Bots by Ensembles of Specialized Classifiers

Sayyadiharikandeh, M., Varol, O., Yang, K.-C., Flammini, A., & Menczer, F. (2020) · Proceedings of the 29th ACM International Conference on Information & Knowledge Management

Spam and Phishing DetectionMisinformation and Its Impacts

Cited by 159

Considering Temporal Variations of Spatial Visual Distortions in Video Quality Assessment

Ninassi, A., Le Meur, O., Le Callet, P., & Barba, D. (2009) · IEEE Journal of Selected Topics in Signal Processing

Image and Video Quality AssessmentVideo Coding and Compression Technologies

Cited by 159

Reconstructing an image from its local descriptors

Weinzaepfel, P., Jegou, H., & Perez, P. (2011) · CVPR 2011

Advanced Image and Video Retrieval TechniquesImage Retrieval and Classification Techniques

Cited by 159

Dealing with the Effects of Sensor Displacement in Wearable Activity Recognition

Banos, O., Toth, M., Damas, M., Pomares, H., & Rojas, I. (2014) · Sensors

Context-Aware Activity Recognition SystemsPhysical Activity and Health

Cited by 159

Visual SLAM for autonomous ground vehicles

Lategahn, H., Geiger, A., & Kitt, B. (2011) · 2011 IEEE International Conference on Robotics and Automation

Robotics and Sensor-Based LocalizationAdvanced Vision and Imaging

Cited by 159

The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

Gehrmann, S., Adewumi, T., Aggarwal, K., Ammanamanchi, P. S., Aremu, A., Bosselut, A., Chandu, K. R., Clinciu, M.-A., Das, D., Dhole, K., et al. (2021) · Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)

Natural Language Processing TechniquesTopic Modeling

Cited by 159

The Trotter step size required for accurate quantum simulation of quantum chemistry

Poulin, D., Hastings, M. B., Wecker, D., Wiebe, N., Doberty, A. C., & Troyer, M. (2015) · Quantum Information and Computation

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 159

SciREX: A Challenge Dataset for Document-Level Information Extraction

Jain, S., van Zuylen, M., Hajishirzi, H., & Beltagy, I. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Topic ModelingBiomedical Text Mining and Ontologies

Cited by 159

PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering

Hyun Cho, J., Mall, U., Bala, K., & Hariharan, B. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 159

The Use of Digital Health in the Detection and Management of COVID-19

Alwashmi, M. (2020) · International Journal of Environmental Research and Public Health

COVID-19 Digital Contact TracingData-Driven Disease Surveillance

Cited by 159

Application-Oriented Performance Benchmarks for Quantum Computing

Lubinski, T., Johri, S., Varosy, P., Coleman, J., Zhao, L., Necaise, J., Baldwin, C. H., Mayer, K., & Proctor, T. (2021) · arXiv

Quantum Computing Algorithms and ArchitectureParallel Computing and Optimization Techniques

Cited by 159

A Dual Privacy Preserving Scheme in Continuous Location-Based Services

Zhang, S., Wang, G., Bhuiyan, M. Z. A., & Liu, Q. (2018) · IEEE Internet of Things Journal

Privacy-Preserving Technologies in DataPrivacy, Security, and Data Protection

Cited by 159

Flag fault-tolerant error correction with arbitrary distance codes

Chamberland, C., & Beverland, M. E. (2018) · Quantum

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 159

An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models

Chen, L., Zhao, H., Liu, T., Bai, S., Lin, J., Zhou, C., & Chang, B. (2024) · arXiv

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 159

Blockchain for public services: a systematic literature review.

Cagigas, D., Clifton, J., Diaz-Fuentes, D., & Fernandez-Gutierrez, M. (2021) · IEEE Access

Blockchain Technology Applications and SecurityE-Government and Public Services

Cited by 159

Implications of AI (un-)fairness in higher education admissions: the effects of perceived AI (un-)fairness on exit, voice and organizational reputation

Marcinkowski, F., Kieslich, K., Starke, C., & Lünich, M. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIArtificial Intelligence in Education

Cited by 159

TransformEHR: transformer-based encoder-decoder generative model to enhance prediction of disease outcomes using electronic health records

Yang, Z., Mitra, A., Liu, W., Berlowitz, D., & Yu, H. (2023) · Nature Communications

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 158

Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review

Kenner, B., Chari, S. T., Kelsen, D., Klimstra, D. S., Pandol, S. J., Rosenthal, M., Rustgi, A. K., Taylor, J. A., Yala, A., Abul-Husn, N., et al. (2021) · Pancreas

AI in cancer detectionPancreatic and Hepatic Oncology Research

Cited by 158

SwapAdvisor: Pushing Deep Learning Beyond the GPU Memory Limit via Smart Swapping

Huang, C.-C., Jin, G., & Li, J. (2020) · Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems

Parallel Computing and Optimization TechniquesAdvanced Neural Network Applications

Cited by 158

A parallel algorithm for real-time computation of optical flow

Bülthoff, H., Little, J., & Poggio, T. (1989) · Nature

Advanced Vision and ImagingVisual perception and processing mechanisms

Cited by 158

AI auditing: The Broken Bus on the Road to AI Accountability

Birhane, A., Steed, R., Ojewale, V., Vecchione, B., & Raji, I. D. (2024) · 2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)

Ethics and Social Impacts of AIDigitalization, Law, and Regulation

Cited by 158

A Double Deep Q-Learning Model for Energy-Efficient Edge Scheduling

Zhang, Q., Lin, M., Yang, L. T., Chen, Z., Khan, S. U., & Li, P. (2019) · IEEE Transactions on Services Computing

IoT and Edge/Fog ComputingEnergy Efficiency in Computing

Cited by 158

All Tokens Matter: Token Labeling for Training Better Vision Transformers

Jiang, Z., Hou, Q., Yuan, L., Zhou, D., Shi, Y., Jin, X., Wang, A., & Feng, J. (2021) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 158

A Comprehensive Survey on Deep Learning Multi-Modal Fusion: Methods, Technologies and Applications

Jiao, T., Guo, C., Feng, X., Chen, Y., & Song, J. (2024) · Computers, Materials & Continua

Multimodal Machine Learning ApplicationsAdvanced Image Fusion Techniques

Cited by 158