Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Gradient-based learning applied to document recognition

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

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Going Deeper with Convolutions

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Enhancing scientific discoveries in molecular biology with deep generative models

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QuipuNet: Convolutional Neural Network for Single-Molecule Nanopore Sensing

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ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions

Gao, H., Wang, Z., Cai, L., & Ji, S. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2975796 · Cited by 81

The effects of data sampling with deep learning and highly imbalanced big data

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In Search of Lost Domain Generalization

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Interpretable deep learning for guided microstructure-property explorations in photovoltaics

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Transfer learning for predicting conversion from mild cognitive impairment to dementia of Alzheimer's type based on a three-dimensional convolutional neural network

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On Warm-Starting Neural Network Training

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Deep neural networks predicting oil movement in a development unit

Temirchev, P., Simonov, M., Kostoev, R., Burnaev, E., Oseledets, I., Akhmetov, A., Margarit, A., Sitnikov, A., & Koroteev, D. (2020) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2019.106513 · Cited by 75

Reinforcement learning-based adaptive PID controller for DPS

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Cross and learn: Cross-modal self-supervision

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Autoregressive Unsupervised Image Segmentation

Ouali, Y., Hudelot, C., & Tami, M. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58571-6_9 · Cited by 75

Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence

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Optimizing Agent Behavior over Long Time Scales by Transporting Value

Hung, C.-C., Lillicrap, T., Abramson, J., Wu, Y., Mirza, M., Carnevale, F., Ahuja, A., & Wayne, G. (2018) · arXiv · doi:10.48550/arxiv.1810.06721 · Cited by 75

Subject-Aware Contrastive Learning for Biosignals

Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., & Azemi, E. (2020) · arXiv · doi:10.48550/arxiv.2007.04871 · Cited by 74

Fast vehicle detection in UAV images

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Group emotion recognition with individual facial emotion CNNs and global image based CNNs

Tan, L., Zhang, K., Wang, K., Zeng, X., Peng, X., & Qiao, Y. (2017) · Proceedings of the 19th ACM International Conference on Multimodal Interaction · doi:10.1145/3136755.3143008 · Cited by 73

A Disentangling Invertible Interpretation Network for Explaining Latent Representations

Esser, P., Rombach, R., & Ommer, B. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00924 · Cited by 72

DriveGAN: Towards a Controllable High-Quality Neural Simulation

Kim, S. W., Philion, J., Torralba, A., & Fidler, S. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00576 · Cited by 72

Artificial intelligence utilising corneal confocal microscopy for the diagnosis of peripheral neuropathy in diabetes mellitus and prediabetes

Preston, F. G., Meng, Y., Burgess, J., Ferdousi, M., Azmi, S., Petropoulos, I. N., Kaye, S., Malik, R. A., Zheng, Y., & Alam, U. (2022) · Diabetologia · doi:10.1007/s00125-021-05617-x · Cited by 70

Cloud Versus Edge Deployment Strategies of Real-Time Face Recognition Inference

Koubaa, A., Ammar, A., Kanhouch, A., & AlHabashi, Y. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3055835 · Cited by 69

Robust application of new deep learning tools: an experimental study in medical imaging

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Bayesian convolutional neural network based MRI brain extraction on nonhuman primates

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Integrating uncertainty in deep neural networks for MRI based stroke analysis

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Classification-Driven Dynamic Image Enhancement

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Fdftnet: facing off fake images using fake detection fine-tuning network

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Acoustic Classification of Surface and Underwater Vessels in the Ocean Using Supervised Machine Learning

Choi, J., Choo, Y., & Lee, K. (2019) · Sensors · doi:10.3390/s19163492 · Cited by 63

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Empirical asset pricing via machine learning: evidence from the European stock market

Drobetz, W., & Otto, T. (2021) · Journal of Asset Management · doi:10.1057/s41260-021-00237-x · Cited by 61

Learning Semantic Deformation Flows with 3D Convolutional Networks

Yumer, M. E., & Mitra, N. J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_18 · Cited by 60

Multi-representational learning for Offline Signature Verification using Multi-Loss Snapshot Ensemble of CNNs

Masoudnia, S., Mersa, O., Araabi, B. N., Vahabie, A.-H., Sadeghi, M. A., & Ahmadabadi, M. N. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.03.040 · Cited by 57

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Learning Deep Representations of Medical Images using Siamese CNNs with Application to Content-Based Image Retrieval

Chung, Y.-A., & Weng, W.-H. (2017) · arXiv · doi:10.48550/arxiv.1711.08490 · Cited by 58

Lessons Learned from Designing an AI-Enabled Diagnosis Tool for Pathologists

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Character-Level Language Modeling with Deeper Self-Attention

Al-Rfou, R., Choe, D., Constant, N., Guo, M., & Jones, L. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33013159 · Cited by 56

Evolving the olfactory system with machine learning

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Petascale neural circuit reconstruction: automated methods

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Artificial intelligence and its potential in oncology

Londhe, V. Y., & Bhasin, B. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2018.10.005 · Cited by 53

Multimodal Gesture Recognition Using Multi-stream Recurrent Neural Network

Nishida, N., & Nakayama, H. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-29451-3_54 · Cited by 53

Multi-Modal Sensor Fusion-Based Semantic Segmentation for Snow Driving Scenarios

Vachmanus, S., Ravankar, A. A., Emaru, T., & Kobayashi, Y. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3077029 · Cited by 53

Privacy-Preserving Deep Learning Based on Multiparty Secure Computation: A Survey

Zhang, Q., Xin, C., & Wu, H. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3058638 · Cited by 53

Learning Spatio-temporal features to detect manipulated facial videos created by the Deepfake techniques

Nguyen, X. H., Tran, T. S., Le, V. T., Nguyen, K. D., & Truong, D.-T. (2021) · Forensic Science International: Digital Investigation · doi:10.1016/j.fsidi.2021.301108 · Cited by 53

Improving Augmented Human Intelligence to Distinguish Burkitt Lymphoma From Diffuse Large B-Cell Lymphoma Cases

Mohlman, J. S., Leventhal, S. D., Hansen, T., Kohan, J., Pascucci, V., & Salama, M. E. (2020) · American Journal of Clinical Pathology · doi:10.1093/ajcp/aqaa001 · Cited by 50

Uncertainty Estimation via Stochastic Batch Normalization

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A volumetric deep Convolutional Neural Network for simulation of mock dark matter halo catalogues

Berger, P., & Stein, G. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty2949 · Cited by 50

EPNet: Learning to Exit with Flexible Multi-Branch Network

Dai, X., Kong, X., & Guo, T. (2020) · Proceedings of the 29th ACM International Conference on Information & Knowledge Management · doi:10.1145/3340531.3411973 · Cited by 48

The Robustness of Modern Deep Learning Architectures against Single Event Upset Errors

Arechiga, A. P., & Michaels, A. J. (2018) · 2018 IEEE High Performance extreme Computing Conference (HPEC) · doi:10.1109/hpec.2018.8547532 · Cited by 47

P3SGD: Patient Privacy Preserving SGD for Regularizing Deep CNNs in Pathological Image Classification

Wu, B., Zhao, S., Sun, G., Zhang, X., Su, Z., Zeng, C., & Liu, Z. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00220 · Cited by 44

Self-Supervised Wasserstein Pseudo-Labeling for Semi-Supervised Image Classification

Taherkhani, F., Dabouei, A., Soleymani, S., Dawson, J., & Nasrabadi, N. M. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01209 · Cited by 45

Query-Key Normalization for Transformers

Henry, A., Dachapally, P. R., Pawar, S. S., & Chen, Y. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.379 · Cited by 45

Multimodal and Multi-view Models for Emotion Recognition

Aguilar, G., Rozgic, V., Wang, W., & Wang, C. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1095 · Cited by 45

Automated 3D ultrasound image analysis for first trimester assessment of fetal health

Ryou, H., Yaqub, M., Cavallaro, A., Papageorghiou, A. T., & Alison Noble, J. (2019) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ab3ad1 · Cited by 44

Automated ultrasound assessment of amniotic fluid index using deep learning

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Crosstalk Removal in Forward Scan Sonar Image Using Deep Learning for Object Detection

Sung, M., Cho, H., Kim, T., Joe, H., & Yu, S.-C. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2925830 · Cited by 43

RLDS: An explainable residual learning diagnosis system for fetal congenital heart disease

Qiao, S., Pang, S., Luo, G., Pan, S., Yu, Z., Chen, T., & Lv, Z. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2021.10.001 · Cited by 42

ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond

Qiao, S., Shen, W., Qiu, W., Liu, C., & Yuille, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.199 · Cited by 41

Transatlantic transferability of a new reinforcement learning model for optimizing haemodynamic treatment for critically ill patients with sepsis

Roggeveen, L., el Hassouni, A., Ahrendt, J., Guo, T., Fleuren, L., Thoral, P., Girbes, A. R., Hoogendoorn, M., & Elbers, P. W. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.102003 · Cited by 41

Intraoperative stent segmentation in X-ray fluoroscopy for endovascular aortic repair

Breininger, K., Albarqouni, S., Kurzendorfer, T., Pfister, M., Kowarschik, M., & Maier, A. (2018) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-018-1779-6 · Cited by 38

Passive mobile sensing and psychological traits for large scale mood prediction

Spathis, D., Servia-Rodriguez, S., Farrahi, K., Mascolo, C., & Rentfrow, J. (2019) · Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare · doi:10.1145/3329189.3329213 · Cited by 40

Adversarial Multi-view Networks for Activity Recognition

Bai, L., Yao, L., Wang, X., Kanhere, S. S., Guo, B., & Yu, Z. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3397323 · Cited by 40

Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks

Zhang, Z., Li, J., Shao, W., Peng, Z., Zhang, R., Wang, X., & Luo, P. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00364 · Cited by 38

A Strategy for an Uncompromising Incremental Learner

Venkatesan, R., Venkateswara, H., Panchanathan, S., & Li, B. (2017) · arXiv · doi:10.48550/arxiv.1705.00744 · Cited by 37

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Differentiable strong lensing: uniting gravity and neural nets through differentiable probabilistic programming

Chianese, M., Coogan, A., Hofma, P., Otten, S., & Weniger, C. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa1477 · Cited by 36

Recognition of facial expression of fetuses by artificial intelligence (AI)

Miyagi, Y., Hata, T., Bouno, S., Koyanagi, A., & Miyake, T. (2021) · Journal of Perinatal Medicine · doi:10.1515/jpm-2020-0537 · Cited by 34

Improved training of binary networks for human pose estimation and image recognition

Bulat, A., Tzimiropoulos, G., Kossaifi, J., & Pantic, M. (2019) · arXiv · doi:10.48550/arxiv.1904.05868 · Cited by 34

Efficient Exact Verification of Binarized Neural Networks

Jia, K., & Rinard, M. (2020) · arXiv · doi:10.48550/arxiv.2005.03597 · Cited by 33

Passive sonar automated target classifier for shallow waters using end-to-end learnable deep convolutional LSTMs

Kamal, S., Satheesh Chandran, C., & Supriya, M. H. (2021) · Engineering Science and Technology, an International Journal · doi:10.1016/j.jestch.2021.01.014 · Cited by 33

Optimization of the area under the ROC curve using neural network supervectors for text-dependent speaker verification

Mingote, V., Miguel, A., Ortega, A., & Lleida, E. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101078 · Cited by 32

Merged-LSTM and multistep prediction of daily chlorophyll-a concentration for algal bloom forecast

Cho, H., & Park, H. (2019) · IOP Conference Series: Earth and Environmental Science · doi:10.1088/1755-1315/351/1/012020 · Cited by 32

Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D Ultrasound

Yang, X., Dou, H., Huang, R., Xue, W., Huang, Y., Qian, J., Zhang, Y., Luo, H., Guo, H., Wang, T., et al. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3069663 · Cited by 32

Sch-net: a deep learning architecture for automatic detection of schizophrenia

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Moving in the Right Direction: A Regularization for Deep Metric Learning

Mohan, D. D., Sankaran, N., Fedorishin, D., Setlur, S., & Govindaraju, V. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01460 · Cited by 31

Automatic Speech Assessment for People with Aphasia Using TDNN-BLSTM with Multi-Task Learning

Qin, Y., Lee, T., Feng, S., & Kong, A. P. H. (2018) · Interspeech 2018 · doi:10.21437/interspeech.2018-1630 · Cited by 30

Multimodal modeling of collaborative problem-solving facets in triads

Stewart, A. E. B., Keirn, Z., & D’Mello, S. K. (2021) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-021-09290-y · Cited by 30

Learning Robust Options

Mankowitz, D., Mann, T., Bacon, P.-L., Precup, D., & Mannor, S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12115 · Cited by 30

Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement

Tanno, R., Worrall, D., Kaden, E., Ghosh, A., Grussu, F., Bizzi, A., Sotiropoulos, S. N., Criminisi, A., & Alexander, D. C. (2019) · arXiv · doi:10.48550/arxiv.1907.13418 · Cited by 30

Best practices in convolutional networks for forward-looking sonar image recognition

Valdenegro-Toro, M. (2017) · OCEANS 2017 - Aberdeen · doi:10.1109/oceanse.2017.8084987 · Cited by 30

Unsupervised Monocular Depth Estimation with Left-Right Consistency

Godard, C., Mac Aodha, O., & Brostow, G. J. (2016) · arXiv · doi:10.48550/arxiv.1609.03677 · Cited by 28

Surfel convolutional neural network for support detection in additive manufacturing

Huang, J., Kwok, T.-H., Zhou, C., & Xu, W. (2019) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-019-03792-1 · Cited by 26

Exploring ROI size in deep learning based lipreading

Koumparoulis, A., Potamianos, G., Mroueh, Y., & Rennie, S. J. (2017) · The 14th International Conference on Auditory-Visual Speech Processing · doi:10.21437/avsp.2017-13 · Cited by 21

A New Deep Transfer Learning Network for Fault Diagnosis of Rotating Machine Under Variable Working Conditions

Qian, W., Li, S., Wang, J., Xin, Y., & Ma, H. (2018) · 2018 Prognostics and System Health Management Conference (PHM-Chongqing) · doi:10.1109/phm-chongqing.2018.00180 · Cited by 28

Adversarial Deep Feature Extraction Network for User Independent Human Activity Recognition

Suh, S., Rey, V. F., & Lukowicz, P. (2022) · 2022 IEEE International Conference on Pervasive Computing and Communications (PerCom) · doi:10.1109/percom53586.2022.9762387 · Cited by 25

Online learning for the hyoid bone tracking during swallowing with neck movement adjustment using semantic segmentation

Lee, D., Lee, W. H., Seo, H. G., Oh, B.-M., Lee, J. C., & Kim, H. C. (2020) · IEEE Access · doi:10.1109/access.2020.3019532 · Cited by 22

Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species

Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2018) · doi:10.1101/478503 · Cited by 21

MaxDropout: Deep Neural Network Regularization Based on Maximum Output Values

do Santos, C. F. G., Colombo, D., Roder, M., & Papa, J. P. (2021) · 2020 25th International Conference on Pattern Recognition (ICPR) · doi:10.1109/icpr48806.2021.9412733 · Cited by 20

Decoding visual motions from EEG using attention based RNN.

Yang, D., Liu, Y., Zhou, Z., Yu, Y., & Liang, X. (2020) · Applied Sciences · doi:10.3390/app10165662 · Cited by 20

Learning Invariant Representations of Social Media Users

Andrews, N., & Bishop, M. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1178 · Cited by 19

Image-based Super Resolution of Underwater Sonar Images using Generative Adversarial Network

Sung, M., Kim, J., & Yu, S.-C. (2018) · TENCON 2018 - 2018 IEEE Region 10 Conference · doi:10.1109/tencon.2018.8650176 · Cited by 18

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Weakly Supervised Learning of Placental Ultrasound Images with Residual Networks

Qi, H., Collins, S., & Noble, A. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-60964-5_9 · Cited by 17

Robust Cross-View Gait Recognition with Evidence: A Discriminant Gait GAN (DiGGAN) Approach

Hu, B., Guan, Y., Gao, Y., Long, Y., Lane, N., & Ploetz, T. (2018) · arXiv · doi:10.48550/arxiv.1811.10493 · Cited by 16

A Hybrid Deep Learning Framework using CNN and GRU-based RNN for Recognition of Pairwise Similar Activities

Siraj, M. S., & Ahad, M. A. R. (2020) · 2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR) · doi:10.1109/icievicivpr48672.2020.9306630 · Cited by 15

IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video for Human Activity Recognition

Kwon, H., Tong, C., Haresamudram, H., Gao, Y., Abowd, G. D., Lane, N. D., & Ploetz, T. (2020) · arXiv · doi:10.48550/arxiv.2006.05675 · Cited by 14

Deep Learning as a Mixed Convex-Combinatorial Optimization Problem

Friesen, A. L., & Domingos, P. (2017) · arXiv · doi:10.48550/arxiv.1710.11573 · Cited by 14

Deep-learning-based precipitation observation quality control

Sha, Y., Gagne, D. J., West, G., & Stull, R. (2021) · Journal of Atmospheric and Oceanic Technology · doi:10.1175/jtech-d-20-0081.1 · Cited by 13

Variance Networks: When Expectation Does Not Meet Your Expectations

Neklyudov, K., Molchanov, D., Ashukha, A., & Vetrov, D. (2018) · arXiv · doi:10.48550/arxiv.1803.03764 · Cited by 12

On Periodic Functions as Regularizers for Quantization of Neural Networks

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Deep Learning from Crowds

Rodrigues, F., & Pereira, F. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11506 · Cited by 10

Unsupervised 3D Reconstruction from a Single Image via Adversarial Learning

Wang, L., & Fang, Y. (2017) · arXiv · doi:10.48550/arxiv.1711.09312 · Cited by 9