Multi-column deep neural networks for image classification

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805

Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex

Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680

Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position

Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759

Greedy layer-wise training of deep networks

Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841

Hierarchical models of object recognition in cortex

Riesenhuber, M., & Poggio, T. (1999) · Nature Neuroscience · doi:10.1038/14819 · Cited by 3,309

Learning methods for generic object recognition with invariance to pose and lighting

LeCun, Y., Huang, F. J., & Bottou, L. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315150 · Cited by 1,324

Untitled work

(2011) · doi:10.5591/978-1-57735-516-8/ijcai11-210 · Cited by 1,229

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741

Deep learning in neural networks: an overview

Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570

Sequence to Sequence Learning with Neural Networks

Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766

Visualizing and understanding convolutional networks

Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839

Representation Learning: A Review and New Perspectives

Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001

Practical Bayesian Optimization of Machine Learning Algorithms

Snoek, J., Larochelle, H., & Adams, R. P. (2012) · arXiv · doi:10.48550/arxiv.1206.2944 · Cited by 6,691

Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review

Rawat, W., & Wang, Z. (2017) · Neural Computation · doi:10.1162/neco_a_00990 · Cited by 3,503

A survey of deep neural network architectures and their applications

Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.12.038 · Cited by 3,239

Randaugment: Practical automated data augmentation with a reduced search space

Cubuk, E. D., Zoph, B., Shlens, J., & Le, Q. V. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00359 · Cited by 3,223

Data-intensive applications, challenges, techniques and technologies: A survey on Big Data

Philip Chen, C. L., & Zhang, C.-Y. (2014) · Information Sciences · doi:10.1016/j.ins.2014.01.015 · Cited by 3,047

All-optical machine learning using diffractive deep neural networks

Lin, X., Rivenson, Y., Yardimci, N. T., Veli, M., Luo, Y., Jarrahi, M., & Ozcan, A. (2018) · Science · doi:10.1126/science.aat8084 · Cited by 2,568

Deep learning for visual understanding: A review

Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.09.116 · Cited by 2,504

Untitled work

(2013) · Cited by 2,056

Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks

Cireşan, D. C., Giusti, A., Gambardella, L. M., & Schmidhuber, J. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_51 · Cited by 1,536

Convolutional neural networks at constrained time cost

He, K., & Sun, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299173 · Cited by 1,558

Quantum-chemical insights from deep tensor neural networks

Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R., & Tkatchenko, A. (2017) · Nature Communications · doi:10.1038/ncomms13890 · Cited by 1,445

Deep Convolutional Network Cascade for Facial Point Detection

Sun, Y., Wang, X., & Tang, X. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.446 · Cited by 1,382

Untitled work

(2012) · neural information processing systems · Cited by 1,291

Evaluating the Visualization of What a Deep Neural Network Has Learned

Samek, W., Binder, A., Montavon, G., Lapuschkin, S., & Muller, K.-R. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2599820 · Cited by 1,164

Convolutional neural networks for large-scale remote-sensing image classification

Maggiori, E., Tarabalka, Y., Charpiat, G., & Alliez, P. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2612821 · Cited by 1,114

DeepTox: toxicity prediction using deep learning

Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S. (2016) · Frontiers in Environmental Science · doi:10.3389/fenvs.2015.00080 · Cited by 1,081

Imitation Learning: A Survey of Learning Methods

Hussein, A., Gaber, M. M., Elyan, E., & Jayne, C. (2018) · ACM Computing Surveys · doi:10.1145/3054912 · Cited by 1,060

Speech emotion recognition using deep 1D & 2D CNN LSTM networks

Zhao, J., Mao, X., & Chen, L. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.08.035 · Cited by 1,044

Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users

Jospin, L. V., Laga, H., Boussaid, F., Buntine, W., & Bennamoun, M. (2022) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2022.3155327 · Cited by 879

State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems

Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2707140 · Cited by 810

A New Representation of Skeleton Sequences for 3D Action Recognition

Ke, Q., Bennamoun, M., An, S., Sohel, F., & Boussaid, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.486 · Cited by 793

A Survey of Optimization Methods From a Machine Learning Perspective

Sun, S., Cao, Z., Zhu, H., & Zhao, J. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2950779 · Cited by 789

Multi-Classification of Brain Tumor Images Using Deep Neural Network

Sultan, H. H., Salem, N. M., & Al-Atabany, W. (2019) · IEEE Access · doi:10.1109/access.2019.2919122 · Cited by 775

Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data

Jo, T., Nho, K., & Saykin, A. J. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00220 · Cited by 724

Automatically learning semantic features for defect prediction

Wang, S., Liu, T., & Tan, L. (2016) · Proceedings of the 38th International Conference on Software Engineering · doi:10.1145/2884781.2884804 · Cited by 689

Deep learning for molecular design—a review of the state of the art

Elton, D. C., Boukouvalas, Z., Fuge, M. D., & Chung, P. W. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00039a · Cited by 683

A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots

Giusti, A., Guzzi, J., Ciresan, D. C., He, F.-L., Rodriguez, J. P., Fontana, F., Faessler, M., Forster, C., Schmidhuber, J., Caro, G. D., et al. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2015.2509024 · Cited by 665

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

Adversarial NLI: A New Benchmark for Natural Language Understanding

Nie, Y., Williams, A., Dinan, E., Bansal, M., Weston, J., & Kiela, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.441 · Cited by 656

Untitled work

(2013) · Cited by 651

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

Ball, J. E., Anderson, D. T., & Chan, C. S. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.042609 · Cited by 746

Within the lack of chest COVID-19 X-ray dataset: A novel detection model based on GAN and deep transfer learning

Loey, M., Smarandache, F., & M. Khalifa, N. E. (2020) · Symmetry · doi:10.3390/sym12040651 · Cited by 538

The neural architecture of language: Integrative modeling converges on predictive processing

Schrimpf, M., Blank, I. A., Tuckute, G., Kauf, C., Hosseini, E. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2105646118 · Cited by 517

Data Augmentation Using Random Image Cropping and Patching for Deep CNNs

Takahashi, R., Matsubara, T., & Uehara, K. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2935128 · Cited by 477

Deep learning in microscopy image analysis: a survey

Xing, F., Xie, Y., Su, H., Liu, F., & Yang, L. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2766168 · Cited by 460

Predicting Parameters in Deep Learning

Shakibi, B. (2014) · University of British Columbia · doi:10.14288/1.0165555 · Cited by 462

DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks

Rajchl, M., Lee, M. C. H., Oktay, O., Kamnitsas, K., Passerat-Palmbach, J., Bai, W., Damodaram, M., Rutherford, M. A., Hajnal, J. V., Kainz, B., et al. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2621185 · Cited by 410

Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey

Buhrmester, V., Münch, D., & Arens, M. (2021) · Machine Learning and Knowledge Extraction · doi:10.3390/make3040048 · Cited by 403

Automatic moth detection from trap images for pest management

Ding, W., & Taylor, G. (2016) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2016.02.003 · Cited by 367

Improving Traffic Flow Prediction With Weather Information in Connected Cars: A Deep Learning Approach

Koesdwiady, A., Soua, R., & Karray, F. (2016) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2016.2585575 · Cited by 367

Fingerprint Liveness Detection Using Convolutional Neural Networks

Nogueira, R. F., de Alencar Lotufo, R., & Campos Machado, R. (2016) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2520880 · Cited by 359

An artificial intelligence platform for the multihospital collaborative management of congenital cataracts

Long, E., Lin, H., Liu, Z., Wu, X., Wang, L., Jiang, J., An, Y., Lin, Z., Li, X., Chen, J., et al. (2017) · Nature Biomedical Engineering · doi:10.1038/s41551-016-0024 · Cited by 358

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

Some Improvements on Deep Convolutional Neural Network Based Image Classification

Howard, A. G. (2013) · arXiv · doi:10.48550/arxiv.1312.5402 · Cited by 356

Compute Trends Across Three Eras of Machine Learning

Sevilla, J., Heim, L., Ho, A., Besiroglu, T., Hobbhahn, M., & Villalobos, P. (2022) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9891914 · Cited by 363

A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data

Sun, D., Wang, M., & Li, A. (2019) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2806438 · Cited by 338

Fractional Max-Pooling

Graham, B. (2014) · arXiv · doi:10.48550/arxiv.1412.6071 · Cited by 337

Deep learning in robotics: A review of recent research

Pierson, H. A., & Gashler, M. S. (2017) · Advanced Robotics · doi:10.1080/01691864.2017.1365009 · Cited by 331

Regularized Evolution for Image Classifier Architecture Search

Real, E., Aggarwal, A., Huang, Y., & Le, Q. V. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33014780 · Cited by 328

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

Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology

Sharma, H., Zerbe, N., Klempert, I., Hellwich, O., & Hufnagl, P. (2017) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2017.06.001 · Cited by 312

Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN)

Ahlawat, S., Choudhary, A., Nayyar, A., Singh, S., & Yoon, B. (2020) · Sensors · doi:10.3390/s20123344 · Cited by 303

Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification

Rodriguez, P., Cucurull, G., Gonzalez, J., Gonfaus, J. M., Nasrollahi, K., Moeslund, T. B., & Roca, F. X. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2662199 · Cited by 297

Automatic breast ultrasound image segmentation: a survey

Xian, M., Zhang, Y., Cheng, H. D., Xu, F., Zhang, B., & Ding, J. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.02.012 · Cited by 284

Automatic Shadow Detection and Removal from a Single Image

Khan, S. H., Bennamoun, M., Sohel, F., & Togneri, R. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2462355 · Cited by 283

A Survey of Handwritten Character Recognition with MNIST and EMNIST

Baldominos, A., Saez, Y., & Isasi, P. (2019) · Applied Sciences · doi:10.3390/app9153169 · Cited by 279

AdaBoost-CNN: An adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learning

Taherkhani, A., Cosma, G., & McGinnity, T. M. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.03.064 · Cited by 281

Memristor-Based Multilayer Neural Networks With Online Gradient Descent Training

Soudry, D., Di Castro, D., Gal, A., Kolodny, A., & Kvatinsky, S. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2383395 · Cited by 271

Mining opinions from instructor evaluation reviews: a deep learning approach

Onan, A. (2020) · Computer Applications in Engineering Education · doi:10.1002/cae.22179 · Cited by 270

Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels

Kostrikov, I., Yarats, D., & Fergus, R. (2020) · arXiv · doi:10.48550/arxiv.2004.13649 · Cited by 267

Contrastive Learning with Stronger Augmentations

Wang, X., & Qi, G.-J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3203630 · Cited by 266

Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study

Alzubaidi, L., Fadhel, M. A., Al-Shamma, O., Zhang, J., Santamaría, J., Duan, Y., & R. Oleiwi, S. (2020) · Applied Sciences · doi:10.3390/app10134523 · Cited by 258

Brain Tumor Detection Using Deep Neural Network and Machine Learning Algorithm

Siar, M., & Teshnehlab, M. (2019) · 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) · doi:10.1109/iccke48569.2019.8964846 · Cited by 247

Analyzing and Increasing the Reliability of Convolutional Neural Networks on GPUs

Santos, F. F. D., Pimenta, P. F., Lunardi, C., Draghetti, L., Carro, L., Kaeli, D., & Rech, P. (2019) · IEEE Transactions on Reliability · doi:10.1109/tr.2018.2878387 · Cited by 226

Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks

Ma, J., Wu, F., Jiang, T., Zhao, Q., & Kong, D. (2017) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-017-1649-7 · Cited by 187

Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations

Tobore, I., Li, J., Yuhang, L., Al-Handarish, Y., Kandwal, A., Nie, Z., & Wang, L. (2019) · JMIR mHealth and uHealth · doi:10.2196/11966 · Cited by 180

Non-iterative and Fast Deep Learning: Multilayer Extreme Learning Machines

Zhang, J., Li, Y., Xiao, W., & Zhang, Z. (2020) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2020.04.033 · Cited by 179

. Hyper-class augmented and regularized deep learning for fine-grained image classification

Xie, S., Yang, T., Wang, X., & Lin, Y. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298880 · Cited by 169

House Price Prediction Using Machine Learning and Neural Networks

Varma, A., Sarma, A., Doshi, S., & Nair, R. (2018) · 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT) · doi:10.1109/icicct.2018.8473231 · Cited by 157

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 · doi:10.1609/aaai.v30i1.10243 · Cited by 147

Marginal space deep learning: efficient architecture for volumetric image parsing

Ghesu, F. C., Krubasik, E., Georgescu, B., Singh, V., Zheng, Y., Hornegger, J., & Comaniciu, D. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2538802 · Cited by 142

Cascade convolutional neural networks for automatic detection of thyroid nodules in ultrasound images

Ma, J., Wu, F., Jiang, T., Zhu, J., & Kong, D. (2017) · Medical Physics · doi:10.1002/mp.12134 · Cited by 142

A deep convolutional neural network method to detect seizures and characteristic frequencies using epileptic electroencephalogram (EEG) data

Rashed-Al-Mahfuz, M., Moni, M. A., Uddin, S., Alyami, S. A., Summers, M. A., & Eapen, V. (2021) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2021.3050925 · Cited by 138

The neural architecture of language: Integrative modeling converges on predictive processing

(2020) · doi:10.1101/2020.06.26.174482 · Cited by 129

Robot Learning From Randomized Simulations: A Review

Muratore, F., Ramos, F., Turk, G., Yu, W., Gienger, M., & Peters, J. (2022) · Frontiers in Robotics and AI · doi:10.3389/frobt.2022.799893 · Cited by 128

Learning for Personalized Medicine: A Comprehensive Review From a Deep Learning Perspective

Zhang, S., Bamakan, S. M. H., Qu, Q., & Li, S. (2019) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2018.2864254 · Cited by 125

Deep learning in biomedical data science

Baldi, P. (2018) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-080917-013343 · Cited by 119

Morpheus: A Deep Learning Framework for the Pixel-level Analysis of Astronomical Image Data

Hausen, R., & Robertson, B. E. (2020) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ab8868 · Cited by 110

Neural networks for computer-aided diagnosis in medicine: A review

lin, D., Vasilakos, A. V., Tang, Y., & Yao, Y. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.08.039 · Cited by 105

Multicolumn bidirectional long short-term memory for mobile devices-based human activity recognition

Tao, D., Wen, Y., & Hong, R. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2561962 · Cited by 83

Vision-Language Navigation with Random Environmental Mixup

Liu, C., Zhu, F., Chang, X., Liang, X., Ge, Z., & Shen, Y.-D. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00167 · Cited by 80

Perceptual Image Hashing for Content Authentication Based on Convolutional Neural Network With Multiple Constraints

Qin, C., Liu, E., Feng, G., & Zhang, X. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.3047142 · Cited by 78

Deep Feature Learning with Discrimination Mechanism for Brain Tumor Segmentation and Diagnosis

Zhao, L., & Jia, K. (2015) · 2015 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP) · doi:10.1109/iih-msp.2015.41 · Cited by 67

Real-Time Reservoir Computing Network-Based Systems for Detection Tasks on Visual Contents

Jalalvand, A., Van Wallendael, G., & Van De Walle, R. (2015) · 2015 7th International Conference on Computational Intelligence, Communication Systems and Networks · doi:10.1109/cicsyn.2015.35 · Cited by 63

Counterexample-Guided Data Augmentation

Dreossi, T., Ghosh, S., Yue, X., Keutzer, K., Sangiovanni-Vincentelli, A., & Seshia, S. A. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/286 · Cited by 60

Deep Big Multilayer Perceptrons for Digit Recognition

Cireşan, D. C., Meier, U., Gambardella, L. M., & Schmidhuber, J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_31 · Cited by 48

Scaling up data curation using deep learning: An application to literature triage in genomic variation resources

Lee, K., Famiglietti, M. L., McMahon, A., Wei, C.-H., MacArthur, J. A. L., Poux, S., Breuza, L., Bridge, A., Cunningham, F., Xenarios, I., et al. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006390 · Cited by 47

Toward Transportation Mode Recognition Using Deep Convolutional and Long Short-Term Memory Recurrent Neural Networks

Qin, Y., Luo, H., Zhao, F., Wang, C., Wang, J., & Zhang, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2944686 · Cited by 46

Multi-clue fusion for emotion recognition in the wild

Yan, J., Zheng, W., Cui, Z., Tang, C., Zhang, T., Zong, Y., & Sun, N. (2016) · Proceedings of the 18th ACM International Conference on Multimodal Interaction · doi:10.1145/2993148.2997630 · Cited by 42

When Video meets Inertial Sensors: Zero-shot Domain Adaptation for Finger Motion Analytics with Inertial Sensors

Liu, Y., Zhang, S., & Gowda, M. (2021) · Proceedings of the International Conference on Internet-of-Things Design and Implementation · doi:10.1145/3450268.3453537 · Cited by 39

Development of Gas–Liquid Flow Regimes Identification Using a Noninvasive Ultrasonic Sensor, Belt-Shape Features, and Convolutional Neural Network in an S-Shaped Riser

Nnabuife, S. G., Kuang, B., Whidborne, J. F., & Rana, Z. A. (2023) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3084860 · Cited by 32

Research on fault diagnosis of hydraulic pump using convolutional neural network

Yan, J., Zhu, H., Yang, X., Cao, Y., & Shao, L. (2016) · Journal of Vibroengineering · doi:10.21595/jve.2016.16956 · Cited by 30

Using deep learning and electronic health records to detect Noonan syndrome in pediatric patients

Yang, Z., Shikany, A., Ni, Y., Zhang, G., Weaver, K. N., & Chen, J. (2022) · Genetics in Medicine · doi:10.1016/j.gim.2022.08.002 · Cited by 20

Detecting clinical medication errors with AI enabled wearable cameras

Chan, J., Nsumba, S., Wortsman, M., Dave, A., Schmidt, L., Gollakota, S., & Michaelsen, K. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01295-2 · Cited by 18