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| == References == | | == References == |
| [1] Min Lin, Qiang Chen, and Shuicheng Yan. Network in network. CoRR, abs/1312.4400, 2013.
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| [2] Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Computer Vision and Pattern Recognition, 2014. CVPR 2014. IEEE Conference on, 2014.
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| [3] Sanjeev Arora, Aditya Bhaskara, Rong Ge, and Tengyu Ma. Provable bounds for learning some deep representations. CoRR, abs/1310.6343, 2013.
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| [4] ¨Umit V. C¸ ataly¨urek, Cevdet Aykanat, and Bora Uc¸ar. On two-dimensional sparse matrix partitioning: Models, methods, and a recipe. SIAM J. Sci. Comput., 32(2):656–683, February 2010.
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| Footnote 1: Hebbian theory is a neuroscientific theory claiming that an increase in synaptic
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| efficacy arises from a presynaptic cell's repeated and persistent stimulation of a postsynaptic
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| cell. It is an attempt to explain synaptic plasticity, the adaptation of brain neurons during the learning process.
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| Footnote 2: Fore more explanation on 1 by 1 convolution refer to: https://iamaaditya.github.io/2016/03/one-by-one-convolution/
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