Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position

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

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

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

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

Untitled work

(1989) · neural information processing systems · Cited by 3,736

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

Convolutional networks and applications in vision

LeCun, Y., Kavukcuoglu, K., & Farabet, C. (2010) · Proceedings of 2010 IEEE International Symposium on Circuits and Systems · doi:10.1109/iscas.2010.5537907 · Cited by 2,213

A Survey of the Usages of Deep Learning for Natural Language Processing

Otter, D. W., Medina, J. R., & Kalita, J. K. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2979670 · Cited by 1,836

Building high-level features using large scale unsupervised learning

Le, Q. V. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639343 · Cited by 1,695

Object Recognition with Gradient-Based Learning

LeCun, Y., Haffner, P., Bottou, L., & Bengio, Y. (1999) · Lecture Notes in Computer Science · doi:10.1007/3-540-46805-6_19 · Cited by 947

Deep Learning in Drug Discovery

Gawehn, E., Hiss, J. A., & Schneider, G. (2016) · Molecular Informatics · doi:10.1002/minf.201501008 · Cited by 723

Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study

Yasaka, K., Akai, H., Abe, O., & Kiryu, S. (2018) · Radiology · doi:10.1148/radiol.2017170706 · Cited by 663

The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations

Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2023) · AI & SOCIETY · doi:10.1007/s00146-021-01294-x · Cited by 552

The AI Gambit — Leveraging Artificial Intelligence to Combat Climate Change: Opportunities, Challenges, and Recommendations

Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2021) · doi:10.2139/ssrn.3804983 · Cited by 552

Vehicle Type Classification Using a Semisupervised Convolutional Neural Network

Dong, Z., Wu, Y., Pei, M., & Jia, Y. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2402438 · Cited by 387

Deep learning with convolutional neural network in radiology

Yasaka, K., Akai, H., Kunimatsu, A., Kiryu, S., & Abe, O. (2018) · Japanese Journal of Radiology · doi:10.1007/s11604-018-0726-3 · Cited by 368

Development of convolutional neural network and its application in image classification: a survey

Wang, W., Yang, Y., Wang, X., Wang, W., & Li, J. (2019) · Optical Engineering · doi:10.1117/1.oe.58.4.040901 · Cited by 252

The hardware lottery

Hooker, S. (2021) · Communications of the ACM · doi:10.1145/3467017 · Cited by 211

Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors

Moya Rueda, F., Grzeszick, R., Fink, G. A., Feldhorst, S., & Ten Hompel, M. (2018) · Informatics · doi:10.3390/informatics5020026 · Cited by 196

Weight Agnostic Neural Networks

Gaier, A., & Ha, D. (2019) · Unpublished · doi:10.13140/rg.2.2.16025.88169 · Cited by 161

Visual Selective Behavior Can Be Triggered by a Feed-Forward Process

VanRullen, R., & Koch, C. (2003) · Journal of Cognitive Neuroscience · doi:10.1162/089892903321208141 · Cited by 140

An analog neural network processor with programmable topology

Boser, B. E., Sackinger, E., Bromley, J., Le Cun, Y., & Jackel, L. D. (1991) · IEEE Journal of Solid-State Circuits · doi:10.1109/4.104196 · Cited by 142

Technical and clinical overview of deep learning in radiology

Ueda, D., Shimazaki, A., & Miki, Y. (2019) · Japanese Journal of Radiology · doi:10.1007/s11604-018-0795-3 · Cited by 100

A Deep Learning Perspective on the Origin of Facial Expressions

Breuer, R., & Kimmel, R. (2017) · arXiv · doi:10.48550/arxiv.1705.01842 · Cited by 92

Hybrid Network Model for “Deep Learning” of Chemical Data: Application to Antimicrobial Peptides

Schneider, P., Müller, A. T., Gabernet, G., Button, A. L., Posselt, G., Wessler, S., Hiss, J. A., & Schneider, G. (2017) · Molecular Informatics · doi:10.1002/minf.201600011 · Cited by 52

Differentiating Alzheimer’s Disease from Dementia with Lewy Bodies Using a Deep Learning Technique Based on Structural Brain Connectivity

Wada, A., Tsuruta, K., Irie, R., Kamagata, K., Maekawa, T., Fujita, S., Koshino, S., Kumamaru, K., Suzuki, M., Nakanishi, A., et al. (2019) · Magnetic Resonance in Medical Sciences · doi:10.2463/mrms.mp.2018-0091 · Cited by 37