LIBLINEAR: A Library for Large Linear Classification

LIBSVM: A library for support vector machines

Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918

Distributional Structure

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Scikit-learn: Machine Learning in Python

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XGBoost: A Scalable Tree Boosting System

Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569

The WEKA data mining software: an update

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750

Learning Deep Features for Discriminative Localization

Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.319 · Cited by 11,428

DeepWalk: online learning of social representations

Perozzi, B., Al-Rfou, R., & Skiena, S. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623732 · Cited by 10,123

Domain-Adversarial Training of Neural Networks

Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2017) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-3-319-58347-1_10 · Cited by 8,318

Deep Learning Face Attributes in the Wild

Liu, Z., Luo, P., Wang, X., & Tang, X. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.425 · Cited by 7,747

Places: A 10 Million Image Database for Scene Recognition

Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2723009 · Cited by 4,042

NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis

Shahroudy, A., Liu, J., Ng, T.-T., & Wang, G. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.115 · Cited by 3,148

Structural Deep Network Embedding

Wang, D., Cui, P., & Zhu, W. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939753 · Cited by 2,845

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(2014) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 2,534

AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild

Mollahosseini, A., Hasani, B., & Mahoor, M. H. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2740923 · Cited by 2,338

Deep Hashing Network for Unsupervised Domain Adaptation

Venkateswara, H., Eusebio, J., Chakraborty, S., & Panchanathan, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.572 · Cited by 2,171

Certifying and Removing Disparate Impact

Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., & Venkatasubramanian, S. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783311 · Cited by 1,827

Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach

Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M. E. P., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073791 · Cited by 1,700

Learning Rich Features from RGB-D Images for Object Detection and Segmentation

Gupta, S., Girshick, R., Arbeláez, P., & Malik, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10584-0_23 · Cited by 1,528

Hypercolumns for object segmentation and fine-grained localization

Hariharan, B., Arbelaez, P., Girshick, R., & Malik, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298642 · Cited by 1,511

Potential of fecal microbiota for early-stage detection of colorectal cancer

Zeller, G., Tap, J., Voigt, A. Y., Sunagawa, S., Kultima, J. R., Costea, P. I., Amiot, A., Böhm, J., Brunetti, F., Habermann, N., et al. (2014) · Molecular Systems Biology · doi:10.15252/msb.20145645 · Cited by 1,375

Do Better ImageNet Models Transfer Better?

Kornblith, S., Shlens, J., & Le, Q. V. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00277 · Cited by 1,325

A Survey on Network Embedding

Cui, P., Wang, X., Pei, J., & Zhu, W. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2849727 · Cited by 1,274

EmotionMeter: A Multimodal Framework for Recognizing Human Emotions

Zheng, W.-L., Liu, W., Lu, Y., Lu, B.-L., & Cichocki, A. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2797176 · Cited by 1,276

Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research

Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015) · European Journal of Operational Research · doi:10.1016/j.ejor.2015.05.030 · Cited by 1,284

Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification

Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., & Qin, B. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.3115/v1/p14-1146 · Cited by 1,181

Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model

Letham, B., Rudin, C., McCormick, T. H., & Madigan, D. (2015) · The Annals of Applied Statistics · doi:10.1214/15-aoas848 · Cited by 766

Network Representation Learning: A Survey

Zhang, D., Yin, J., Zhu, X., & Zhang, C. (2020) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2018.2850013 · Cited by 714

Domain Generalization for Object Recognition with Multi-task Autoencoders

Ghifary, M., Kleijn, W. B., Zhang, M., & Balduzzi, D. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.293 · Cited by 664

Identification of an Intestinal Microbiota Signature Associated With Severity of Irritable Bowel Syndrome

Tap, J., Derrien, M., Törnblom, H., Brazeilles, R., Cools-Portier, S., Doré, J., Störsrud, S., Le Nevé, B., Öhman, L., & Simrén, M. (2017) · Gastroenterology · doi:10.1053/j.gastro.2016.09.049 · Cited by 642

Probability Models for Open Set Recognition

Scheirer, W. J., Jain, L. P., & Boult, T. E. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2321392 · Cited by 630

Prototypical Contrastive Learning of Unsupervised Representations

Li, J., Zhou, P., Xiong, C., & Hoi, S. C. H. (2020) · arXiv · doi:10.48550/arxiv.2005.04966 · Cited by 611

Detect What You Can: Detecting and Representing Objects Using Holistic Models and Body Parts

Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., & Yuille, A. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.254 · Cited by 605

Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons

Habib, N., Li, Y., Heidenreich, M., Swiech, L., Avraham-Davidi, I., Trombetta, J. J., Hession, C., Zhang, F., & Regev, A. (2016) · Science · doi:10.1126/science.aad7038 · Cited by 601

Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning

Kemker, R., Salvaggio, C., & Kanan, C. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.04.014 · Cited by 558

DeepInf: Social Influence Prediction with Deep Learning

Qiu, J., Tang, J., Ma, H., Dong, Y., Wang, K., & Tang, J. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3220077 · Cited by 587

Integrating time from experience in the lateral entorhinal cortex

Tsao, A., Sugar, J., Lu, L., Wang, C., Knierim, J. J., Moser, M.-B., & Moser, E. I. (2018) · Nature · doi:10.1038/s41586-018-0459-6 · Cited by 490

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The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity

Grimm, D. G., Azencott, C., Aicheler, F., Gieraths, U., MacArthur, D. G., Samocha, K. E., Cooper, D. N., Stenson, P. D., Daly, M. J., Smoller, J. W., et al. (2015) · Human Mutation · doi:10.1002/humu.22768 · Cited by 356

Closed-loop stimulation of temporal cortex rescues functional networks and improves memory

Ezzyat, Y., Wanda, P. A., Levy, D. F., Kadel, A., Aka, A., Pedisich, I., Sperling, M. R., Sharan, A. D., Lega, B. C., Burks, A., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-02753-0 · Cited by 347

Learning Expressionlets on Spatio-temporal Manifold for Dynamic Facial Expression Recognition

Liu, M., Shan, S., Wang, R., & Chen, X. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.226 · Cited by 392

High-confidence structural annotation of metabolites absent from spectral libraries

Hoffmann, M. A., Nothias, L.-F., Ludwig, M., Fleischauer, M., Gentry, E. C., Witting, M., Dorrestein, P. C., Dührkop, K., & Böcker, S. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01045-9 · Cited by 388

Sparse Autoencoder-Based Feature Transfer Learning for Speech Emotion Recognition

Deng, J., Zhang, Z., Marchi, E., & Schuller, B. (2013) · 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction · doi:10.1109/acii.2013.90 · Cited by 366

Non-convex Optimization for Machine Learning

Jain, P., & Kar, P. (2017) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000058 · Cited by 364

Glimpse: Continuous, Real-Time Object Recognition on Mobile Devices

Chen, T. Y.-H., Ravindranath, L., Deng, S., Bahl, P., & Balakrishnan, H. (2015) · Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems · doi:10.1145/2809695.2809711 · Cited by 358

Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners

Mei, S., & Zhu, X. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9569 · Cited by 346

Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype

Couture, H. D., Williams, L. A., Geradts, J., Nyante, S. J., Butler, E. N., Marron, J. S., Perou, C. M., Troester, M. A., & Niethammer, M. (2018) · npj Breast Cancer · doi:10.1038/s41523-018-0079-1 · Cited by 327

Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression: A Multimodal, Multisite Machine Learning Analysis

Koutsouleris, N., Kambeitz-Ilankovic, L., Ruhrmann, S., Rosen, M., Ruef, A., Dwyer, D. B., Paolini, M., Chisholm, K., Kambeitz, J., Haidl, T., et al. (2018) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.2165 · Cited by 341

Voting for Voting in Online Point Cloud Object Detection

Zeng Wang, D., & Posner, I. (2015) · Robotics: Science and Systems XI · doi:10.15607/rss.2015.xi.035 · Cited by 337

“Predicting MOOC dropout over weeks using machine learning methods,”

Kloft, M., Stiehler, F., Zheng, Z., & Pinkwart, N. (2014) · Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs · doi:10.3115/v1/w14-4111 · Cited by 325

Text-Based Twitter User Geolocation Prediction

Han, B., Cook, P., & Baldwin, T. (2014) · Journal of Artificial Intelligence Research · doi:10.1613/jair.4200 · Cited by 324

Unsupervised feature learning for 3D scene labeling

Lai, K., Bo, L., & Fox, D. (2014) · 2014 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2014.6907298 · Cited by 317

Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data

Koutsoukas, A., Monaghan, K. J., Li, X., & Huan, J. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0226-y · Cited by 315

Machine Learning for Encrypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity

Anderson, B., & McGrew, D. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098163 · Cited by 295

” How old do you think I am?” A study of language and age in Twitter

Nguyen, D., Gravel, R., Trieschnigg, D., & Meder, T. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14381 · Cited by 287

3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients

Nie, D., Zhang, H., Adeli, E., Liu, L., & Shen, D. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_25 · Cited by 282

Explaining Data-Driven Document Classifications1

Martens, D., & Provost, F. (2014) · MIS Quarterly · doi:10.25300/misq/2014/38.1.04 · Cited by 264

Predicting age and gender in online social networks

Peersman, C., Daelemans, W., & Van Vaerenbergh, L. (2011) · Proceedings of the 3rd international workshop on Search and mining user-generated contents · doi:10.1145/2065023.2065035 · Cited by 260

Using VADER sentiment and SVM for predicting customer response sentiment

Borg, A., & Boldt, M. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113746 · Cited by 251

Guidance for RNA-seq co-expression network construction and analysis: safety in numbers

Ballouz, S., Verleyen, W., & Gillis, J. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv118 · Cited by 215

Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier

Chen, C., Zhang, Q., Yu, B., Yu, Z., Lawrence, P. J., Ma, Q., & Zhang, Y. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103899 · Cited by 238

Authorship Attribution for Social Media Forensics

Rocha, A., Scheirer, W. J., Forstall, C. W., Cavalcante, T., Theophilo, A., Shen, B., Carvalho, A. R. B., & Stamatatos, E. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2603960 · Cited by 222

Large-Scale Cross-Modality Search via Collective Matrix Factorization Hashing

Ding, G., Guo, Y., Zhou, J., & Gao, Y. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2607421 · Cited by 218

Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages

Nie, D., Lu, J., Zhang, H., Adeli, E., Wang, J., Yu, Z., Liu, L., Wang, Q., Wu, J., & Shen, D. (2019) · Scientific Reports · doi:10.1038/s41598-018-37387-9 · Cited by 213

Reducing systematic review workload through certainty-based screening

Miwa, M., Thomas, J., O’Mara-Eves, A., & Ananiadou, S. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.06.005 · Cited by 190

Combining Multiple Kernel Methods on Riemannian Manifold for Emotion Recognition in the Wild

Liu, M., Wang, R., Li, S., Shan, S., Huang, Z., & Chen, X. (2014) · Proceedings of the 16th International Conference on Multimodal Interaction · doi:10.1145/2663204.2666274 · Cited by 202

Learning Deep Network Representations with Adversarially Regularized Autoencoders

Yu, W., Zheng, C., Cheng, W., Aggarwal, C. C., Song, D., Zong, B., Chen, H., & Wang, W. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3220000 · Cited by 188

How do visual explanations foster end users' appropriate trust in machine learning?

Yang, F., Huang, Z., Scholtz, J., & Arendt, D. L. (2020) · Proceedings of the 25th International Conference on Intelligent User Interfaces · doi:10.1145/3377325.3377480 · Cited by 200

A comprehensive survey on word representation models: From classical to state-of-the-art word representation language models

Naseem, U., Razzak, I., Khan, S. K., & Prasad, M. (2021) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3434237 · Cited by 194

Super Normal Vector for Human Activity Recognition with Depth Cameras

Yang, X., & Tian, Y. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2565479 · Cited by 162

Understanding Negative Sampling in Graph Representation Learning

Yang, Z., Ding, M., Zhou, C., Yang, H., Zhou, J., & Tang, J. (2020) · Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3394486.3403218 · Cited by 159

Distributed and efficient object detection in edge computing: Challenges and solutions

Ren, J., Guo, Y., Zhang, D., Liu, Q., & Zhang, Y. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700415 · Cited by 148

Prioritising references for systematic reviews with RobotAnalyst: A user study

Przybyła, P., Brockmeier, A. J., Kontonatsios, G., Le Pogam, M., McNaught, J., von Elm, E., Nolan, K., & Ananiadou, S. (2018) · Research Synthesis Methods · doi:10.1002/jrsm.1311 · Cited by 148

Self-taught object localization with deep networks

Bazzani, L., Bergamo, A., Anguelov, D., & Torresani, L. (2016) · 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2016.7477688 · Cited by 140

Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics1

Martens, D., Provost, F., Clark, J., & de Fortuny, E. J. (2016) · MIS Quarterly · doi:10.25300/misq/2016/40.4.04 · Cited by 140

Plug-and-play control of a brain–computer interface through neural map stabilization

Silversmith, D. B., Abiri, R., Hardy, N. F., Natraj, N., Tu-Chan, A., Chang, E. F., & Ganguly, K. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-0662-5 · Cited by 126

Large Scale Personality Classification of Bloggers

Iacobelli, F., Gill, A. J., Nowson, S., & Oberlander, J. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-24571-8_71 · Cited by 136

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A study in transfer learning: leveraging data from multiple hospitals to enhance hospital-specific predictions

Wiens, J., Guttag, J., & Horvitz, E. (2014) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-002162 · Cited by 123

Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data

Farhan, A. A., Yue, C., Morillo, R., Ware, S., Lu, J., Bi, J., Kamath, J., Russell, A., Bamis, A., & Wang, B. (2016) · 2016 IEEE Wireless Health (WH) · doi:10.1109/wh.2016.7764553 · Cited by 115

Comparison of machine learning models for occupancy prediction in residential buildings using connected thermostat data

Huchuk, B., Sanner, S., & O'Brien, W. (2019) · Building and Environment · doi:10.1016/j.buildenv.2019.106177 · Cited by 108

Object Instance Segmentation and Fine-Grained Localization Using Hypercolumns

Hariharan, B., Arbelaez, P., Girshick, R., & Malik, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2578328 · Cited by 102

Auditing Data Provenance in Text-Generation Models

Song, C., & Shmatikov, V. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330885 · Cited by 103

Binarized attributed network embedding

Yang, H., Pan, S., Zhang, P., Chen, L., Lian, D., & Zhang, C. (2018) · 2018 IEEE International Conference on Data Mining (ICDM) · doi:10.1109/icdm.2018.8626170 · Cited by 102

In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction

Wang, C., Han, B., Patel, B., & Rudin, C. (2023) · Journal of Quantitative Criminology · doi:10.1007/s10940-022-09545-w · Cited by 101

Imbalance-Aware Machine Learning for Predicting Rare and Common Disease-Associated Non-Coding Variants

Schubach, M., Re, M., Robinson, P. N., & Valentini, G. (2017) · Scientific Reports · doi:10.1038/s41598-017-03011-5 · Cited by 97

Easy contextual intent prediction and slot detection

Bhargava, A., Celikyilmaz, A., Hakkani-Tur, D., & Sarikaya, R. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639291 · Cited by 91

Authorship Attribution with Topic Models

Seroussi, Y., Zukerman, I., & Bohnert, F. (2014) · Computational Linguistics · doi:10.1162/coli_a_00173 · Cited by 89

Interpretation of QSAR Models by Coloring Atoms According to Changes in Predicted Activity: How Robust Is It?

Sheridan, R. P. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00825 · Cited by 84

A Machine Learning Approach for Semi-Automated Search and Selection in Literature Studies

Ros, R., Bjarnason, E., & Runeson, P. (2017) · Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering · doi:10.1145/3084226.3084243 · Cited by 80

FRaC: a feature-modeling approach for semi-supervised and unsupervised anomaly detection

Noto, K., Brodley, C., & Slonim, D. (2012) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-011-0234-x · Cited by 79

Automated confidence ranked classification of randomized controlled trial articles: an aid to evidence-based medicine

Cohen, A. M., Smalheiser, N. R., McDonagh, M. S., Yu, C., Adams, C. E., Davis, J. M., & Yu, P. S. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocu025 · Cited by 78

Body Joint Guided 3-D Deep Convolutional Descriptors for Action Recognition

Cao, C., Zhang, Y., Zhang, C., & Lu, H. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2756840 · Cited by 78

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From classification to quantification in tweet sentiment analysis

Gao, W., & Sebastiani, F. (2016) · Social Network Analysis and Mining · doi:10.1007/s13278-016-0327-z · Cited by 75

Facial expression recognition in the wild based on multimodal texture features

Sun, B., Li, L., Zhou, G., & He, J. (2016) · Journal of Electronic Imaging · doi:10.1117/1.jei.25.6.061407 · Cited by 74

GLMix: Generalized Linear Mixed Models For Large-Scale Response Prediction

Zhang, X., Zhou, Y., Ma, Y., Chen, B.-C., Zhang, L., & Agarwal, D. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939684 · Cited by 71

Improving reference prioritisation with PICO recognition.

Brockmeier, A. J., Ju, M., Przybyła, P., & Ananiadou, S. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0992-8 · Cited by 62

Development of a prediction model for pancreatic cancer in patients with type 2 diabetes using logistic regression and artificial neural network models

Hsieh, M. H., Sun, L.-M., Lin, C.-L., Hsieh, M.-J., Hsu, C.-Y., & Kao, C.-H. (2018) · Cancer Management and Research · doi:10.2147/cmar.s180791 · Cited by 71

STC Antispoofing Systems for the ASVspoof2021 Challenge

Tomilov, A., Svishchev, A., Volkova, M., Chirkovskiy, A., Kondratev, A., & Lavrentyeva, G. (2021) · 2021 Edition of the Automatic Speaker Verification and Spoofing Countermeasures Challenge · doi:10.21437/asvspoof.2021-10 · Cited by 71

Spontaneous facial expression in unscripted social interactions can be measured automatically

Girard, J. M., Cohn, J. F., Jeni, L. A., Sayette, M. A., & De la Torre, F. (2015) · Behavior Research Methods · doi:10.3758/s13428-014-0536-1 · Cited by 69

Large linear classification when data cannot fit in memory

Yu, H.-F., Hsieh, C.-J., Chang, K.-W., & Lin, C.-J. (2012) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2086737.2086743 · Cited by 69

Adoption Factors of FinTech: Evidence from an Emerging Economy Country-Wide Representative Sample

Mahmud, K., Joarder, M. M. A., & Muheymin-Us-Sakib, K. (2022) · International Journal of Financial Studies · doi:10.3390/ijfs11010009 · Cited by 68