diff --git a/machine_learning/README.md b/machine_learning/README.md index 7afe3d3..c3b94b1 100644 --- a/machine_learning/README.md +++ b/machine_learning/README.md @@ -1,5 +1,6 @@ # Machine Learning + ## External Papers * [Top 10 algorithms in data mining](http://www.cs.uvm.edu/~icdm/algorithms/10Algorithms-08.pdf) @@ -49,6 +50,11 @@ This seminal paper introduces a method to distill information from an ensemble of neural networks into a single model. +* [Truncation of Wavelet Matrices: Edge Effects and the Reduction of Topological Control](https://reader.elsevier.com/reader/sd/pii/0024379594000395?token=EB0AA78D59A9648480596F018EFB72E0A02FD5FA70326B24B9D501E1A6869FE72CC4D97FA9ACC8BAB56060D6C908EC83) by Freedman + + In this paper by Michael Hartley Freedman, he applies Robion Kirby “torus trick”, via wavelets, to the problem of compression. + + ## Hosted Papers * :scroll: **[A Sparse Johnson-Lindenstrauss Transform](dimensionality_reduction/a-sparse-johnson-lindenstrauss-transform.pdf)** @@ -63,14 +69,11 @@ *Bourgain, Jean, and Jelani Nelson. "Toward a unified theory of sparse dimensionality reduction in euclidean space." arXiv preprint arXiv:1311.2542; Accepted in an AMS Journal but unpublished at the moment (2013). Available: http://arxiv.org/abs/1311.2542* -* :scroll: **[Truncation of Wavelet Matrices: Edge Effects and the Reduction of Topological Control](https://reader.elsevier.com/reader/sd/pii/0024379594000395?token=EB0AA78D59A9648480596F018EFB72E0A02FD5FA70326B24B9D501E1A6869FE72CC4D97FA9ACC8BAB56060D6C908EC83)** by Freedman - - In this paper by Michael Hartley Freedman, he applies Robion Kirby “torus trick”, via wavelets, to the problem of compression. -* :scroll: **[Understanding Deep Convolutional Networks](https://github.com/papers-we-love/papers-we-love/blob/master/machine_learning/Understanding-Deep-Convolutional-Networks.pdf)** by Mallat +* :scroll: **[Understanding Deep Convolutional Networks](Understanding-Deep-Convolutional-Networks.pdf)** by Mallat Stéphane Mallat proposes a model by which renormalisation can identify self-similar structures in deep networks. [This video of Curt MacMullen discussing renormalization](https://www.youtube.com/watch?v=_qjPFF5Gv1I) can help with more context. -* :scroll: **[General self-similarity: an overview](https://github.com/papers-we-love/papers-we-love/blob/master/machine_learning/General-self-similarity--an-overview.pdf)** by Leinster +* :scroll: **[General self-similarity: an overview](General-self-similarity--an-overview.pdf)** by Leinster -Dr Leinster's paper provides a concise, straightforward, picture of self-similarity, and its role in renormalization. \ No newline at end of file + Dr. Leinster's paper provides a concise, straightforward, picture of self-similarity, and its role in renormalization.