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We propose a novel learning paradigm for Deep Neural Networks (DNN) by using Boolean logic algebra.
Neural computing: theory and practice
Wasserman, P. D · 1989
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On the computational power of neural nets
Siegelmann, H. T. and Sontag, E. D · 1992
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Inductive logic programming: Theory and methods
Muggleton, S. and De Raedt, L · 1994
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Approximating the semantics of logic programs by recurrent neural networks
Hölldobler, S., Kalinke, Y., and Störr, H.-P · 1999
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Design of capacity-approaching irregular low-density parity-check codes
Richardson, T. J., Shokrollahi, M. A., and Urbanke, R. L · 2001
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Neural networks–a model of boolean functions
Steinbach, B. and Kohut, R · 2002
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A solution for the n-bit parity problem using a single translated multiplicative neuron
Iyoda, E. M., Nobuhara, H., and Hirota, K · 2003
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K-separability
Duch, W · 2006
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Inductive logic programming in a nutshell
Dzeroski, S · 2007
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Connectionist model generation: A first-order approach
Bader, S., Hitzler, P., and Hölldobler, S · 2008
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A unified architecture for natural language processing: Deep neural networks with multitask learning
Collobert, R. and Weston, J · 2008
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Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition
Dahl, G. E., Yu, D., Deng, L., and Acero, A · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Fast relational learning using bottom clause propositionalization with artificial neural networks
França, M. V., Zaverucha, G., and Garcez, A. S. d · 2014
Logical minimisation of meta-rules within meta-interpretive learning
Cropper, A. and Muggleton, S. H · 2015
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Kaiser, Ł. and Sutskever, I · 2015
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Pointer networks
Vinyals, O., Fortunato, M., and Jaitly, N · 2015
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Metagol system
Cropper, A. and Muggleton, S. H · 2016
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Logic tensor networks: Deep learning and logical reasoning from data and knowledge
Serafini, L. and Garcez, A. d · 2016
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Perceptrons: An introduction to computational geometry
Minsky, M. and Papert, S. A · 2017
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X · 2015
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Learning explanatory rules from noisy data
Evans, R. and Grefenstette, E · 2018
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Decoding ldpc codes on binary erasure channels using deep recurrent neural-logic layers
Payani, A. and Fekri, F · 2018
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