A new variant of restricted Boltzmann machine with horizontal connections
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Restricted Boltzmann machines (RBMs) are successfully employed to construct deep architectures because their power of expression and the inference is tractable and easy. In this paper, we propose a model named self-connected restricted Boltzmann machine (SCRBM), which adds horizontal connections to the hidden layer to enable direct information transfer between hidden units. We present a simple and effective method based on greedy layer-wise procedure of deep learning algorithms to train the model. Under the algorithm, SCRBM has a three-layer architecture. The first hidden layer extracts features from the data, and the second hidden layer is used to stimulate various interactions between units in the layer. Specifically, to stimulate the lateral inhibition that exists in sensory systems, a log sparse item is introduced to the second hidden layer of SCRBM. Our experiments show that the features learned by our algorithm are more vivid and clean than those learned by basic RBM and SparseRBM. Further experiments show the performance of SCRBM outperforms basic RBM and SparseRBM on several widely used datasets in terms of accuracy.
KeywordsNeural networks RBM Horizontal connections Greedy layer-wise learning
This work is supported by the National Basic Research Program of China (973 Program, No. 2013CB329404), the National Natural Science Foundation of China (Nos. 61572393, 11501049, 11671317, 11131006) and the Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase).
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Conflict of interest
The authors declare that they have no conflict of interest.
Human and animal rights
This article does not contain any studies with human participants or animals performed by any of the authors.
- 2.Widrow B, Hoff ME (1962) Associative storage and retrieval of digital information in networks of adaptive neurons. In: Biological prototypes and synthetic systems, Springer US, pp 160–160Google Scholar
- 3.Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp 91–99Google Scholar
- 4.Ciresan DC, Giusti A, Gambardella LM, Schmidhuber J (2013) Mitosis detection in breast cancer histology images with deep neural networks. In: Medical image computing and computer-assisted intervention–MICCAI 2013, Springer, Berlin, pp 411–418Google Scholar
- 6.Dan CC, Giusti A, Gambardella LM, Schmidhuber J (2012) Deep neural networks segment neuronal membranes in electron microscopy images. Adv Neural Inf Process Syst 25:2852–2860Google Scholar
- 8.Von der Malsburg C (1973) Self-organization of orientation sensitive cells in the striate cortex. Biol Cybern 14(2):85–100Google Scholar
- 14.Werbos PJ (1982) Applications of advances in nonlinear sensitivity analysis. In: System modeling and optimization. Springer, Berlin, pp 762–770Google Scholar
- 15.Werbos PJ. Beyond regression: New tools for prediction and analysis in the behavioral sciences, Ph.d. dissertation Harvard UniversityGoogle Scholar
- 17.Bengio Y, Lamblin P, Popovici D, Larochelle H et al (2007) Greedy layer-wise training of deep networks. Adv Neural Inf Process Syst 19:153Google Scholar
- 20.Lee H, Ekanadham C, Ng AY (2008) Sparse deep belief net model for visual area v2. In: Advances in neural information processing systems, vol 20, pp 873–880Google Scholar
- 21.Osindero S, Hinton GE (2008) Modeling image patches with a directed hierarchy of markov random fields. In: Advances in neural information processing systems, pp 1121–1128Google Scholar
- 22.Larochelle H, Erhan D, Vincent P (2009) Deep learning using robust interdependent codes. In: AISTATS, pp 312–319Google Scholar
- 23.Hinton GE, Sejnowski TJ (1986) Learning and relearning in boltzmann machines. Parallel Distrib Process Explor Microstruct Cognit 1:282–317Google Scholar
- 26.Goldstein E (2013) Sensation and perception, Cengage LearningGoogle Scholar
- 27.Welling M, Hinton GE (2002) A new learning algorithm for mean field boltzmann machines. In: International conference on artificial neural networks (ICANN’02), Springer, Berlin, pp 351–357Google Scholar
- 28.Ranzato M, Boureau YL, Lecun Y (2007) Sparse feature learning for deep belief networks. Adv Neural Inf Process Syst 20:1185–1192Google Scholar
- 32.LeCun Y, Huang FJ, Bottou L (2004) Learning methods for generic object recognition with invariance to pose and lighting. In: IEEE computer society conference on computer vision and pattern recognition (CVPR’04), vol 2, IEEE, pp II–97Google Scholar
- 34.Williams CK, Agakov FV. An analysis of contrastive divergence learning in gaussian boltzmann machines. Institute for Adaptive and Neural ComputationGoogle Scholar
- 36.Yuille AL (2005) The convergence of contrastive divergences. In: Advances in neural information processing systems, pp 1593–1600Google Scholar