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mycqstate.wordpress.com | ||
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lucatrevisan.wordpress.com
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| | | | | (This is the sixth in a series of posts on online optimization techniques and their ``applications'' to complexity theory, combinatorics and pseudorandomness. The plan for this series of posts is to alternate one post explaining a result from the theory of online convex optimization and one post explaining an ``application.'' The first two posts were... | |
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www.depthfirstlearning.com
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| | | | | [AI summary] The provided text is a detailed exploration of the mathematical and statistical foundations of neural networks, focusing on the Jacobian matrix, its spectral properties, and the implications for dynamical isometry. The key steps and results are as follows: 1. **Jacobian and Spectral Analysis**: The Jacobian matrix $ extbf{J} $ of a neural network is decomposed into $ extbf{J} = extbf{W} extbf{D} $, where $ extbf{W} $ is the weight matrix and $ extbf{D} $ is a diagonal matrix of derivatives. The spectral properties of $ extbf{J} extbf{J}^T $ are analyzed using the $ S $-transform, which captures the behavior of the eigenvalues of the Jacobian matrix. 2. **$ S $-Transform Derivation**: The $ S $-transform of $ extbf{J} extbf{J}^T $ is... | |
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polymathprojects.org
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| | | | | I've been feeling slightly guilty over the last few days because I've been thinking privately about the problem of improving the Roth bounds. However, the kinds of things I was thinking about felt somehow easier to do on my own, and my plan was always to go public if I had any idea that was... | |
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vcansimplify.wordpress.com
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| | | Recently I have been reading up on frequency domain image processing. I am still just beginning to understand how it works. Over the last few weeks I have been trying to understand the ** Fourier Transform **. Although the gist of Fourier Series is easy to understand from its formula, that of the Fourier Transform... | ||
