|
[1]
|
Hinton, G.E. (2007) To Recognize Shapes, First Learn to Generate Images. 6 King’s College Rd, Toronto, 1-17. [Google Scholar] [CrossRef]
|
|
[2]
|
Hinton, G.E., Dayan, P., Frey, B.J. and Neal, R.M. (1995) The Wake-Sleep Algorithm for Unsupervised Neural Networks. Science, 268, 1158-1161. [Google Scholar] [CrossRef] [PubMed]
|
|
[3]
|
Le, Q.V. (2012) Building High-Level Features Using Large Scale Unsupervised Learning. Proceedings of the 29th International Conference on Machine Learning, Edinburgh, Scotland, UK. https://arxiv.org/abs/1112.6209v5
|
|
[4]
|
Behin-Aein, B., Diep, V. and Datta, S. (2016) A Building Block for Hardware Belief Networks. Scientific Reports, 6, Article No. 29893.
|
|
[5]
|
Hinton, G.E. and Osindero, S. (2006) A Fast Learning Algorithm for Deep Belief Nets. Neural Computation, 18, 1527-1554. [Google Scholar] [CrossRef] [PubMed]
|
|
[6]
|
Mohamed, A.-R., Dahl, G.E. and Hinton, G. (2011) Acoustic Modeling Using Deep Belief Networks. IEEE Transactions on Audio, Speech, and Language Pro-cessing, 20, 14-22.
|
|
[7]
|
Ranzato, M.A., Poultney, C., Chopra, S. and LeCun, Y. (2006) Efficient Learning of Sparse Representations with an Energy-Based Model. Proceedings of the 19th International Conference on Neural Information Processing Systems, Canada, 4-7 December 2006, 1137-1144.
|
|
[8]
|
O’Connor, P., Neil, D., Liu, S.-C., Delbruck, T. and Pfeiffer, M. (2013) Real-Time Classifica-tion and Sensor Fusion with a Spiking Deep Belief Network. Frontiers in Neuroscience, 7, 178. [Google Scholar] [CrossRef] [PubMed]
|
|
[9]
|
Hinton, G.E. and Salakhutdinov, R.R. (2006) Reducing the Dimensionality of Data with Neural Networks. Science, 313, 504-507. [Google Scholar] [CrossRef] [PubMed]
|
|
[10]
|
Fischer, A. and Igel, C. (2014) Training Restricted Boltzmann Machines: An Introduction. Pattern Recognition, 47, 25-39. [Google Scholar] [CrossRef]
|
|
[11]
|
Tang, T.B. and Murray, A.F. (2016) Adaptive Sensor Modelling and Classifi-cation Using a Continuous Restricted Boltzmann Machine (CRBM). Neurocomputing, 70, 1198-1206. [Google Scholar] [CrossRef]
|
|
[12]
|
Salakhutdinov, R., Mnih, A. and Hinton, G. (2007) Restricted Boltzmann Machines for Collaborative Filtering. Proceedings of the 24th International Conference on Machine Learning, Corvalis, Oregon, 20-24 June 2007, 791-798.
|
|
[13]
|
Chen, F.Q., Wu, Y., Bu, Y.D. and Zhao, G.D. (2016) Spectral Classification Using Restricted Boltzmann Machine. Publications of the Astronomical Society of Australia, 31, e0017.
|
|
[14]
|
Li, G.Q., Deng, L., Xu, Y., Wen, C.Y., Wang, W., Pei, J. and Shi, L.P. (2016) Temperature Based Restricted Boltzmann Machines. Scientific Reports, 6, Article No. 19133. [Google Scholar] [CrossRef] [PubMed]
|
|
[15]
|
Vaidyanathan, S. (2015) Adaptive Control of the FitzHugh-Nagumo Chaotic Neuron Model. International Journal of PharmTech Research, 8, 117-127.
|
|
[16]
|
Pantazi, A., Woźniak, S., Tuma, T. and Eleftherio, E. (2016) All-Memristive Neuromorphic Computing with Level-Tuned Neurons. Nanotechnology, 27, Article ID: 355205. [Google Scholar] [CrossRef] [PubMed]
|
|
[17]
|
Ginzburg, S.L. and Pustovoit, M.A. (2006) Response of Hodgkin-Huxley Stochastic Bursting Neuron to Single-Pulse Stimulus. Physica A: Statistical Mechanics and Its Applications, 369, 354-368. [Google Scholar] [CrossRef]
|
|
[18]
|
Zhang, L.H., Zhang, D.S., Deng, Y.Q., Ding, X.Q., Wang, Y., Tang, Y.Y. and Sun, B.L. (2016) A Simplified Computational Memory Model from Information Processing. Scientific Reports, 6, Article No. 37470.
|
|
[19]
|
Han, S., Pool, J., Tran, J. and Dally, W.J. (2015) Learning Both Weights and Connections for Efficient Neural Networks. Proceedings of the 28th International Conference on Neural Information Processing Systems, 1, 1135-1143.
|
|
[20]
|
Bengio, S., Vinyals, O., Jaitly, N. and Shazeer, N. (2015) Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks. Proceedings of the 28th International Conference on Neural Information Processing Systems, 1, 1171-1179.
|
|
[21]
|
Nam, H. and Han, B. (2016) Learning Multi-Domain Convolutional Neural Networks for Visual Tracking. 2016 IEEE Conference on Computer Vision and Pattern Recogni-tion (CVPR), Las Vegas, NV, 27-30 June 2016, 4293-4302. [Google Scholar] [CrossRef]
|
|
[22]
|
Chen, P.-Y., Choudhury, S. and Hero, A.O. (2015) Multi-Centrality Graph Spectral Decompositions and Their Application to Cyber Intrusion Detection. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, 20-25 March 2016, 4553-4557.
|
|
[23]
|
Van den Oord, A., Kalchbrenner, N. and Kavukcuoglu, K. (2016) Pixel Recurrent Neural Networks. Proceedings of the 33rd International Conference on International Conference on Ma-chine Learning, 48, 1747-1756.
|
|
[24]
|
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C. and Torr, P.H.S. (2015) Conditional Random Fields as Recurrent Neural Networks. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 7-13 December 2015, 1529-1537. [Google Scholar] [CrossRef]
|
|
[25]
|
Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H. and Ng, Y. (2016) Multimodal Deep Learning. Proceedings of the 28th International Conference on Machine Learning, Bellevue, 28 June-2 July 2011, 689-696.
|
|
[26]
|
Jozefowicz, R., Zaremba, W. and Sutskever, I. (2016) An Em-pirical Exploration of Recurrent Network Architectures. Proceedings of the 32nd International Conference on International Conference on Machine Learning, 37, 2342-2350.
|