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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Unsupervised Pretraining Transfers Well Across Languages

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I-BERT: Integer-only BERT Quantization

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Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps

Wickstrøm, K., Kampffmeyer, M., & Jenssen, R. (2020) · Medical Image Analysis · doi:10.1016/j.media.2019.101619 · Cited by 185

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Multimodal Transfer: A Hierarchical Deep Convolutional Neural Network for Fast Artistic Style Transfer

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A Systematic Review of Detecting Sleep Apnea Using Deep Learning

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SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning

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Deep Adversarial Learning for Multi-Modality Missing Data Completion

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Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network

Fujioka, T., Kubota, K., Mori, M., Kikuchi, Y., Katsuta, L., Kasahara, M., Oda, G., Ishiba, T., Nakagawa, T., & Tateishi, U. (2019) · Japanese Journal of Radiology · doi:10.1007/s11604-019-00831-5 · Cited by 177

Glow: Generative Flow with Invertible 1x1 Convolutions

Kingma, D. P., & Dhariwal, P. (2018) · arXiv · Cited by 825

Are we done with imagenet?

Beyer, L., Hénaff, O. J., Kolesnikov, A., Zhai, X., & Oord, A. V. D. (2020) · arXiv · doi:10.48550/arxiv.2006.07159 · Cited by 176

Skin disease diagnosis with deep learning: a review

Li, H., Pan, Y., Zhao, J., & Zhang, L. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.096 · Cited by 178

Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

Zrimec, J., Börlin, C. S., Buric, F., Muhammad, A. S., Chen, R., Siewers, V., Verendel, V., Nielsen, J., Töpel, M., & Zelezniak, A. (2020) · Nature Communications · doi:10.1038/s41467-020-19921-4 · Cited by 174

Gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

Zrimec, J., Buric, F., Muhammad, A. S., Chen, R., Verendel, V., Töpel, M., & Zelezniak, A. (2019) · doi:10.1101/792531 · Cited by 173

Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network

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Bringing ai to edge: from deep learning’s perspective

Liu, D., Kong, H., Luo, X., Liu, W., & Subramaniam, R. (2022) · Neurocomputing · doi:10.1016/j.neucom.2021.04.141 · Cited by 171

Deep semantic segmentation for automated driving: Taxonomy, roadmap and challenges

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LocNet: Improving Localization Accuracy for Object Detection

Gidaris, S., & Komodakis, N. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.92 · Cited by 154

Comparing user-dependent and user-independent training of CNN for SSVEP BCI

Ravi, A., Beni, N. H., Manuel, J., & Jiang, N. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab6a67 · Cited by 165

Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation

Chen, L.-C., Lopes, R. G., Cheng, B., Collins, M. D., Cubuk, E. D., Zoph, B., Adam, H., & Shlens, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58545-7_40 · Cited by 165

XNOR-Net++: Improved Binary Neural Networks

Bulat, A., & Tzimiropoulos, G. (2019) · arXiv · doi:10.48550/arxiv.1909.13863 · Cited by 165

Exploiting epistemic uncertainty of anatomy segmentation for anomaly detection in retinal OCT

Seebock, P., Orlando, J. I., Schlegl, T., Waldstein, S. M., Bogunovic, H., Klimscha, S., Langs, G., & Schmidt-Erfurth, U. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2919951 · Cited by 164

BRein Memory: A Single-Chip Binary/Ternary Reconfigurable in-Memory Deep Neural Network Accelerator Achieving 1.4 TOPS at 0.6 W

Ando, K., Ueyoshi, K., Orimo, K., Yonekawa, H., Sato, S., Nakahara, H., Takamaeda-Yamazaki, S., Ikebe, M., Asai, T., Kuroda, T., et al. (2018) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2017.2778702 · Cited by 160

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Deep learning in the automotive industry: Applications and tools

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Single-cell transcriptomic evidence for dense intracortical neuropeptide networks

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Learnable PINs: cross-modal embeddings for person identity

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Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges

Patel, K., Mehta, D., Mistry, C., Gupta, R., Tanwar, S., Kumar, N., & Alazab, M. (2020) · IEEE Access · doi:10.1109/access.2020.2993803 · Cited by 157

TasselNetv2: in-field counting of wheat spikes with context-augmented local regression networks

Xiong, H., Cao, Z., Lu, H., Madec, S., Liu, L., & Shen, C. (2019) · Plant Methods · doi:10.1186/s13007-019-0537-2 · Cited by 157

Adversarial Video Generation on Complex Datasets

Clark, A., Donahue, J., & Simonyan, K. (2019) · arXiv · doi:10.48550/arxiv.1907.06571 · Cited by 157

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., & Plötz, T. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3411841 · Cited by 152

Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit?

Zhu, S., Dong, X., & Su, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00506 · Cited by 153

Deep-Learning-Based Gridded Downscaling of Surface Meteorological Variables in Complex Terrain. Part I: Daily Maximum and Minimum 2-m Temperature

Sha, Y., Gagne II, D. J., West, G., & Stull, R. (2020) · Journal of Applied Meteorology and Climatology · doi:10.1175/jamc-d-20-0057.1 · Cited by 141

Contrastive Predictive Coding for Human Activity Recognition

Haresamudram, H., Essa, I., & Plötz, T. (2021) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3463506 · Cited by 150

Masked reconstruction based self-supervision for human activity recognition

Haresamudram, H., Beedu, A., Agrawal, V., Grady, P. L., Essa, I., Hoffman, J., & Plötz, T. (2020) · Proceedings of the 2020 International Symposium on Wearable Computers · doi:10.1145/3410531.3414306 · Cited by 146

RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data

Xu, Y., Biswal, S., Deshpande, S. R., Maher, K. O., & Sun, J. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3220051 · Cited by 144

Supermasks in Superposition

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A Lightweight YOLOv2: A Binarized CNN with A Parallel Support Vector Regression for an FPGA

Nakahara, H., Yonekawa, H., Fujii, T., & Sato, S. (2018) · Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays · doi:10.1145/3174243.3174266 · Cited by 139

OpenEI: An Open Framework for Edge Intelligence

Zhang, X., Wang, Y., Lu, S., Liu, L., xu, L., & Shi, W. (2019) · 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00182 · Cited by 139

An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks

Seshadri, K., Akin, B., Laudon, J., Narayanaswami, R., & Yazdanbakhsh, A. (2022) · 2022 IEEE International Symposium on Workload Characterization (IISWC) · doi:10.1109/iiswc55918.2022.00017 · Cited by 138

A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research

Watson, C., Cooper, N., Palacio, D. N., Moran, K., & Poshyvanyk, D. (2022) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3485275 · Cited by 137

Two-Step Quantization for Low-bit Neural Networks

Wang, P., Hu, Q., Zhang, Y., Zhang, C., Liu, Y., & Cheng, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00460 · Cited by 136

Vibration-based multiclass damage detection and localization using long short-term memory networks

Sony, S., Gamage, S., Sadhu, A., & Samarabandu, J. (2022) · Structures · doi:10.1016/j.istruc.2021.10.088 · Cited by 140

Integrated multi-omics analysis of ovarian cancer using variational autoencoders

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SYQ: Learning Symmetric Quantization for Efficient Deep Neural Networks

Faraone, J., Fraser, N., Blott, M., & Leong, P. H. W. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00452 · Cited by 134

LambdaNetworks: Modeling Long-Range Interactions Without Attention

Bello, I. (2021) · arXiv · doi:10.48550/arxiv.2102.08602 · Cited by 129

Deep Learning for Distributed Optimization: Applications to Wireless Resource Management

Lee, H., Lee, S. H., & Quek, T. Q. S. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2933890 · Cited by 129

Scale-Aware Face Detection

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Deep-learning model for predicting 30-day postoperative mortality

Fritz, B. A., Cui, Z., Zhang, M., He, Y., Chen, Y., Kronzer, A., Ben Abdallah, A., King, C. R., & Avidan, M. S. (2019) · British Journal of Anaesthesia · doi:10.1016/j.bja.2019.07.025 · Cited by 127

Micro-Batch Training with Batch-Channel Normalization and Weight Standardization

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Mixed-Precision Deep Learning Based on Computational Memory

Nandakumar, S. R., Le Gallo, M., Piveteau, C., Joshi, V., Mariani, G., Boybat, I., Karunaratne, G., Khaddam-Aljameh, R., Egger, U., Petropoulos, A., et al. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00406 · Cited by 120

Towards a predictor for CO2 plume migration using deep neural networks

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Adversarial Attacks on Time Series

Karim, F., Majumdar, S., & Darabi, H. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2986319 · Cited by 118

Reward learning from human preferences and demonstrations in Atari

Ibarz, B., Leike, J., Pohlen, T., Irving, G., Legg, S., & Amodei, D. (2018) · arXiv · doi:10.48550/arxiv.1811.06521 · Cited by 117

Watch this: Scalable cost-function learning for path planning in urban environments

Wulfmeier, M., Wang, D. Z., & Posner, I. (2016) · 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759328 · Cited by 116

Prediction of cardiovascular diseases by integrating multi-modal features with machine learning methods

Li, P., Hu, Y., & Liu, Z.-P. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102474 · Cited by 116

Inception and ResNet features are (almost) equivalent

McNeely-White, D., Beveridge, J. R., & Draper, B. A. (2020) · Cognitive Systems Research · doi:10.1016/j.cogsys.2019.10.004 · Cited by 116

Yolov3-lite: A lightweight crack detection network for aircraft structure based on depthwise separable convolutions

Li, Y., Han, Z., Xu, H., Liu, L., Li, X., & Zhang, K. (2019) · Applied Sciences · doi:10.3390/app9183781 · Cited by 115

Trained Quantization Thresholds for Accurate and Efficient Fixed-Point Inference of Deep Neural Networks

Jain, S. R., Gural, A., Wu, M., & Dick, C. H. (2019) · arXiv · doi:10.48550/arxiv.1903.08066 · Cited by 115

Fetal Ultrasound Image Segmentation for Automatic Head Circumference Biometry Using Deeply Supervised Attention-Gated V-Net

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BodyCompass: Monitoring Sleep Posture with Wireless Signals

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Extracting biological age from biomedical data via deep learning: too much of a good thing?

Pyrkov, T. V., Slipensky, K., Barg, M., Kondrashin, A., Zhurov, B., Zenin, A., Pyatnitskiy, M., Menshikov, L., Markov, S., & Fedichev, P. O. (2018) · Scientific Reports · doi:10.1038/s41598-018-23534-9 · Cited by 111

Generalization in Reinforcement Learning by Soft Data Augmentation

Hansen, N., & Wang, X. (2021) · 2021 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48506.2021.9561103 · Cited by 111

CNN based semantic segmentation for urban traffic scenes using fisheye camera

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Hierarchical 3D fully convolutional networks for multi-organ segmentation

Roth, H. R., Oda, H., Hayashi, Y., Oda, M., Shimizu, N., Fujiwara, M., Misawa, K., & Mori, K. (2017) · arXiv · doi:10.48550/arxiv.1704.06382 · Cited by 111

Self-supervised learning of a facial attribute embedding from video

Wiles, O., Koepke, A. S., & Zisserman, A. (2018) · arXiv · doi:10.48550/arxiv.1808.06882 · Cited by 111

Machine Learning on Mainstream Microcontrollers

Sakr, F., Bellotti, F., Berta, R., & De Gloria, A. (2020) · Sensors · doi:10.3390/s20092638 · Cited by 109

Handling Vanishing Gradient Problem Using Artificial Derivative

Hu, Z., Zhang, J., & Ge, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3054915 · Cited by 109

Big data driven jobs remaining time prediction in discrete manufacturing system: a deep learning-based approach

Fang, W., Guo, Y., Liao, W., Ramani, K., & Huang, S. (2020) · International Journal of Production Research · doi:10.1080/00207543.2019.1602744 · Cited by 109

End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study

Coppock, H., Gaskell, A., Tzirakis, P., Baird, A., Jones, L., & Schuller, B. (2021) · BMJ Innovations · doi:10.1136/bmjinnov-2021-000668 · Cited by 108

Accelerating CNN inference on FPGAs: A Survey

Abdelouahab, K., Pelcat, M., Serot, J., & Berry, F. (2018) · arXiv · doi:10.48550/arxiv.1806.01683 · Cited by 107

Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection

Akcay, S., Atapour-Abarghouei, A., & Breckon, T. P. (2019) · 2019 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8851808 · Cited by 103

Short-Term Load Forecasting based on ResNet and LSTM

Choi, H., Ryu, S., & Kim, H. (2018) · 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) · doi:10.1109/smartgridcomm.2018.8587554 · Cited by 103

AI outperformed every dermatologist in dermoscopic melanoma diagnosis, using an optimized deep-CNN architecture with custom mini-batch logic and loss function

Pham, T.-C., Luong, C.-M., Hoang, V.-D., & Doucet, A. (2021) · Scientific Reports · doi:10.1038/s41598-021-96707-8 · Cited by 101

Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification

Liu, T., & Abd-Elrahman, A. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.03.006 · Cited by 103

Comparative Study of Multitask Toxicity Modeling on a Broad Chemical Space

Sosnin, S., Karlov, D., Tetko, I. V., & Fedorov, M. V. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00685 · Cited by 102

Rocket Launching: A Universal and Efficient Framework for Training Well-Performing Light Net

Zhou, G., Fan, Y., Cui, R., Bian, W., Zhu, X., & Gai, K. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11601 · Cited by 121

Deep Nets: What have They Ever Done for Vision?

Yuille, A. L., & Liu, C. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01405-z · Cited by 98

Breaking the curse of dimensionality to identify causal variants in Breeding 4

Ramstein, G. P., Jensen, S. E., & Buckler, E. S. (2019) · Theoretical and Applied Genetics · doi:10.1007/s00122-018-3267-3 · Cited by 87

CLOSURE: Assessing Systematic Generalization of CLEVR Models

Bahdanau, D., de Vries, H., O'Donnell, T. J., Murty, S., Beaudoin, P., Bengio, Y., & Courville, A. (2019) · arXiv · doi:10.48550/arxiv.1912.05783 · Cited by 96

Improving Atmospheric River Forecasts With Machine Learning

Chapman, W. E., Subramanian, A. C., Delle Monache, L., Xie, S. P., & Ralph, F. M. (2019) · Geophysical Research Letters · doi:10.1029/2019gl083662 · Cited by 96

Deep Learning-Based Extreme Heatwave Forecast

Jacques-Dumas, V., Ragone, F., Borgnat, P., Abry, P., & Bouchet, F. (2022) · Frontiers in Climate · doi:10.3389/fclim.2022.789641 · Cited by 92

Representation Learning of Histopathology Images using Graph Neural Networks

Adnan, M., Kalra, S., & Tizhoosh, H. R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00502 · Cited by 109

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An Animal Detection Pipeline for Identification

Parham, J., Stewart, C., Crall, J., Rubenstein, D., Holmberg, J., & Berger-Wolf, T. (2018) · 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2018.00123 · Cited by 103

Fully Automatic and Real-Time Catheter Segmentation in X-Ray Fluoroscopy

Ambrosini, P., Ruijters, D., Niessen, W. J., Moelker, A., & van Walsum, T. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-66185-8_65 · Cited by 102

Digital Biologically Plausible Implementation of Binarized Neural Networks With Differential Hafnium Oxide Resistive Memory Arrays

Hirtzlin, T., Bocquet, M., Penkovsky, B., Klein, J.-O., Nowak, E., Vianello, E., Portal, J.-M., & Querlioz, D. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.01383 · Cited by 93

Deep Neural Networks with Relativity Learning for facial expression recognition

Guo, Y., Tao, D., Yu, J., Xiong, H., Li, Y., & Tao, D. (2016) · 2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) · doi:10.1109/icmew.2016.7574736 · Cited by 93

BinFI: an efficient fault injector for safety-critical machine learning systems

Chen, Z., Li, G., Pattabiraman, K., & DeBardeleben, N. (2019) · Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis · doi:10.1145/3295500.3356177 · Cited by 91

Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings

Rubin, J., Parvaneh, S., Rahman, A., Conroy, B., & Babaeizadeh, S. (2018) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2018.08.008 · Cited by 92

Human-Like Playtesting with Deep Learning

Gudmundsson, S. F., Eisen, P., Poromaa, E., Nodet, A., Purmonen, S., Kozakowski, B., Meurling, R., & Cao, L. (2018) · 2018 IEEE Conference on Computational Intelligence and Games (CIG) · doi:10.1109/cig.2018.8490442 · Cited by 90

Convolutional neural networks for decoding of covert attention focus and saliency maps for EEG feature visualization

Farahat, A., Reichert, C., Sweeney-Reed, C. M., & Hinrichs, H. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab3bb4 · Cited by 92

Integrated multi-omics analysis using variational autoencoders: Application to Pan-cancer Classification

Zhang, X., Zhang, J., Sun, K., Yang, X., Dai, C., & Guo, Y. (2019) · 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm47256.2019.8983228 · Cited by 93

Measuring the Algorithmic Efficiency of Neural Networks

Hernandez, D., & Brown, T. B. (2020) · arXiv · doi:10.48550/arxiv.2005.04305 · Cited by 89

Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training

Thakur, S., Doshi, J., Pati, S., Rathore, S., Sako, C., Bilello, M., Ha, S. M., Shukla, G., Flanders, A., Kotrotsou, A., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117081 · Cited by 84

Biology and medicine in the landscape of quantum advantages

Cordier, B. A., Sawaya, N. P. D., Guerreschi, G. G., & McWeeney, S. K. (2022) · Journal of The Royal Society Interface · doi:10.1098/rsif.2022.0541 · Cited by 87

Deep-learning-based risk stratification for mortality of patients with acute myocardial infarction

Kwon, J.-M., Jeon, K.-H., Kim, H. M., Kim, M. J., Lim, S., Kim, K.-H., Song, P. S., Park, J., Choi, R. K., & Oh, B.-H. (2019) · PLOS ONE · doi:10.1371/journal.pone.0224502 · Cited by 86

Causal dilated convolutional neural networks for automatic inspection of ultrasonic signals in non-destructive evaluation and structural health monitoring

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