Scaling Learning Algorithms toward AI

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

Reservoir computing approaches to recurrent neural network training

Lukoševičius, M., & Jaeger, H. (2009) · Computer Science Review · doi:10.1016/j.cosrev.2009.03.005 · Cited by 2,956

Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition

Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2012) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2134090 · Cited by 3,011

Deep learning applications and challenges in big data analytics

Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015) · Journal of Big Data · doi:10.1186/s40537-014-0007-7 · Cited by 2,594

Practical Recommendations for Gradient-Based Training of Deep Architectures

Bengio, Y. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_26 · Cited by 2,259

Spectral-spatial classification of hyperspectral data based on deep belief network

Chen, Y., Zhao, X., & Jia, X. (2015) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2015.2388577 · Cited by 1,259

Semantic hashing

Salakhutdinov, R., & Hinton, G. (2009) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2008.11.006 · Cited by 1,246

A deep learning framework for neuroscience

Richards, B. A., Lillicrap, T. P., Beaudoin, P., Bengio, Y., Bogacz, R., Christensen, A., Clopath, C., Costa, R. P., de Berker, A., Ganguli, S., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0520-2 · Cited by 1,027

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

Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order

Lopes, A. T., de Aguiar, E., De Souza, A. F., & Oliveira-Santos, T. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.07.026 · Cited by 799

Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces

Cecotti, H., & Graser, A. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.125 · Cited by 778

Applications of Deep Learning in Biomedicine

Mamoshina, P., Vieira, A., Lane, E., & Zhavoronkov, A. (2016) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.5b00982 · Cited by 701

Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease

Liu, S., Liu, S., Cai, W., Che, H., Pujol, S., Kikinis, R., Feng, D., Fulham, M. J., & ADNI (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2372011 · Cited by 637

Structural damage identification based on autoencoder neural networks and deep learning

Pathirage, C. S. N., Li, J., Li, L., Hao, H., Liu, W., & Ni, P. (2018) · Engineering Structures · doi:10.1016/j.engstruct.2018.05.109 · Cited by 415

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

An algorithm based on deep learning for predicting in-hospital cardiac arrest

Kwon, J., Lee, Y., Lee, Y., Lee, S., & Park, J. (2018) · Journal of the American Heart Association · doi:10.1161/jaha.118.008678 · Cited by 299

A patch-based convolutional neural network for remote sensing image classification

Sharma, A., Liu, X., Yang, X., & Shi, D. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.07.017 · Cited by 260

Recent advances of deep learning in bioinformatics and computational biology

Tang, B., Pan, Z., Yin, K., & Khateeb, A. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00214 · Cited by 233

Multiple episodes of interbreeding between Neanderthal and modern humans

Villanea, F. A., & Schraiber, J. G. (2018) · Nature Ecology & Evolution · doi:10.1038/s41559-018-0735-8 · Cited by 192

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

Single-Trial Classification of Event-Related Potentials in Rapid Serial Visual Presentation Tasks Using Supervised Spatial Filtering

Cecotti, H., Eckstein, M. P., & Giesbrecht, B. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2302898 · Cited by 109

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

Using Deep Data Augmentation Training to Address Software and Hardware Heterogeneities in Wearable and Smartphone Sensing Devices

Mathur, A., Zhang, T., Bhattacharya, S., Velickovic, P., Joffe, L., Lane, N. D., Kawsar, F., & Lio, P. (2018) · 2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) · doi:10.1109/ipsn.2018.00048 · Cited by 70

Construction of a batch-normalized autoencoder network and its application in mechanical intelligent fault diagnosis

Wang, J., Li, S., Han, B., An, Z., Xin, Y., Qian, W., & Wu, Q. (2019) · Measurement Science and Technology · doi:10.1088/1361-6501/aaf319 · Cited by 61

Deep learning algorithms for traffic forecasting: a comprehensive review and comparison with classical ones

Afandizadeh, S., Abdolahi, S., & Mirzahossein, H. (2024) · Journal of Advanced Transportation · doi:10.1155/2024/9981657 · Cited by 64

Introspective Neural Networks for Generative Modeling

Lazarow, J., Jin, L., & Tu, Z. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.302 · Cited by 49