LCD: A Fast Contrastive Divergence Based Algorithm for Restricted Boltzmann Machine
Tài liệu tham khảo
Blackford, 2002, An updated set of basic linear algebra subprograms (blas), ACM Transactions on Mathematical Software, 28, 135, 10.1145/567806.567807
Bell, 2017, Multitask learning of context-dependent targets in deep neural network acoustic models, IEEE/ACM TASLP, 25, 238
Bengio, 2009, Learning deep architectures for ai, Foundations and Trends in Machine Learning, 2, 1, 10.1561/2200000006
Bengio, 2007, Greedy layer-wise training of deep networks, 153
Cao, 2016, Deepqa: improving the estimation of single protein model quality with deep belief networks, BMC Bioinformatics, 17, 495, 10.1186/s12859-016-1405-y
Chen, 2015, Trans-species learning of cellular signaling systems with bimodal deep belief networks, Bioinformatics, 31, 3008, 10.1093/bioinformatics/btv315
Cho, 2011, Enhanced gradient and adaptive learning rate for training restricted boltzmann machines
Cho, 2013, Gaussian-Bernoulli deep boltzmann machine, 1
Courville, 2011, A spike and slab restricted boltzmann machine, 233
Dahl, 2012, Training restricted boltzmann machines on word observations
Dahl, 2010, Phone recognition with the mean-covariance restricted boltzmann machine, 469
Dahl, 2012, Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition, IEEE TASLP, 20, 30
Deoras, 2013, Deep belief network based semantic taggers for spoken language understanding, 2713
Hinton, G. (2010). A practical guide to training restricted boltzmann machines. Tech. rep..
Hinton., 2006, A fast learning algorithm for deep belief nets, Neural Computation, 18, 1527, 10.1162/neco.2006.18.7.1527
Hinton, 2006, Reducing the dimensionality of data with neural networks, Science, 313, 504, 10.1126/science.1127647
Huang, 2013, Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers, 7304
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014). Caffe: convolutional architecture for fast feature embedding. ArXiv Preprint arXiv:1408.5093.
Karpathy, 2014, Large-scale video classification with convolutional neural networks, 1725
Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, 1097
Lawrence, 1997, Face recognition: a convolutional neural-network approach, IEEE Transactions on Neural Networks, 8, 98, 10.1109/72.554195
LeCun, 1998, Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86, 2278, 10.1109/5.726791
Lee, 2015, Boosted categorical restricted boltzmann machine for computational prediction of splice junctions, 2483
Liu, 2015, De novo identification of replication-timing domains in the human genome by deep learning, Bioinformatics
Marlin, 2010, Inductive principles for restricted boltzmann machine learning, 509
Mohamed, 2009, Deep belief networks for phone recognition, 39
Mohamed, 2012, Acoustic modeling using deep belief networks, IEEE Transactions on Audio, Speech, and Language Processing, 20, 14, 10.1109/TASL.2011.2109382
Mohamed, 2010, PHONE recognition using restricted boltzmann machines, 4354
Mohamed, 2011, Deep belief networks using discriminative features for phone recognition, 5060
Nair, 2009, 3D Object Recognition with Deep Belief Nets, 1339
Nair, 2010, Rectified linear units improve restricted boltzmann machines, 807
Ning, 2017, Lcd: a fast contrastive divergence based algorithm for restricted boltzmann machine
Pinaya, 2016, Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia, Scientific Reports, 6, 10.1038/srep38897
Plis, S. M., Hjelm, D. R., Salakhutdinov, R., & Calhoun, V. D. (2013). Deep learning for neuroimaging: a validation study. ArXiv Preprint arXiv:1312.5847.
Ranzato, 2010, Factored 3-way restricted boltzmann machines for modeling natural images, 621
Salakhutdinov, 2010, Learning deep boltzmann machines using adaptive mcmc, 943
Salakhutdinov, 2009, Deep boltzmann machine, 448
Sarikaya, 2014, Application of deep belief networks for natural language understanding, IEEE/ACM TASLP, 22, 778
Schmah, 2008, Generative versus discriminative training of rbms for classification of fmri images, 1409
Spencer, 2015, A deep learning network approach to ab initio protein secondary structure prediction, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 12, 103, 10.1109/TCBB.2014.2343960
Srivastava, 2012, Multimodal learning with deep boltzmann machines, 2222
Tang, Y., & Sutskever, I. (2011). Data normalization in learning of restricted boltzmann machines. Tech. rep. Department of Computer Science, University of Toronto.
Tieleman, 2008, Training restricted boltzmann machines using approximations to the likelihood gradient, 1064
Tieleman, 2009, Using fast weights to improve persistent contrastive divergence, 1033
Tran, 2013, Thurstonian boltzmann machines: learning from multiple inequalities
Turner, 2014, Deep belief networks used on high resolution multichannel electroencephalography data for seizure detection
Wang, 2014, Relaxations for inference in restricted boltzmann machines
Yamashita, 2014, To be bernoulli or to be gaussian, for a restricted boltzmann machine, 1520
Zaremba, W., Sutskever, I., & Vinyals, O. (2014). Recurrent neural network regularization. ArXiv Preprint arXiv:1409.2329.
Zhang, 2016, A deep learning framework for modeling structural features of rna-binding protein targets, Nucleic Acids Research, 44, e32, 10.1093/nar/gkv1025