Training of Sparsely Connected MLPs
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[1] Geoffrey E. Hinton,et al. Learning representations by back-propagation errors, nature , 1986 .
[2] Kanter,et al. Eigenvalues of covariance matrices: Application to neural-network learning. , 1991, Physical review letters.
[3] Chih-Jen Lin,et al. LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..
[4] Marc'Aurelio Ranzato,et al. Sparse Feature Learning for Deep Belief Networks , 2007, NIPS.
[5] E. Callaway,et al. Excitatory cortical neurons form fine-scale functional networks , 2005, Nature.
[6] Bernhard Schölkopf,et al. Improving the Accuracy and Speed of Support Vector Machines , 1996, NIPS.
[7] Harris Drucker,et al. Comparison of learning algorithms for handwritten digit recognition , 1995 .
[8] Günther Palm,et al. Supervised Matrix Factorization with sparseness constraints and fast inference , 2011, The 2011 International Joint Conference on Neural Networks.
[9] Patrik O. Hoyer,et al. Non-negative Matrix Factorization with Sparseness Constraints , 2004, J. Mach. Learn. Res..
[10] Christopher M. Bishop,et al. Neural networks for pattern recognition , 1995 .
[11] Luca Maria Gambardella,et al. Deep, Big, Simple Neural Nets for Handwritten Digit Recognition , 2010, Neural Computation.
[12] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[13] Fabian J. Theis,et al. Extended Sparse Nonnegative Matrix Factorization , 2005, IWANN.
[14] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[15] Toshihisa Tanaka,et al. First results on uniqueness of sparse non-negative matrix factorization , 2005, 2005 13th European Signal Processing Conference.
[16] Patrice Y. Simard,et al. Best practices for convolutional neural networks applied to visual document analysis , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..
[17] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[18] Bernhard Schölkopf,et al. Training Invariant Support Vector Machines , 2002, Machine Learning.
[19] David L. Elliott,et al. A Better Activation Function for Artificial Neural Networks , 1993 .
[20] Steve B. Furber,et al. Optimal connectivity in hardware-targetted MLP networks , 2009, 2009 International Joint Conference on Neural Networks.
[21] David J. Field,et al. What Is the Goal of Sensory Coding? , 1994, Neural Computation.
[22] H. Sebastian Seung,et al. Learning the parts of objects by non-negative matrix factorization , 1999, Nature.
[23] Antonio Cañas,et al. Towards an Optimal Implementation of MLP in FPGA , 2006, ARC.