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improved the annotations for papers in the Machine Learning readme
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# Data Compression
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* :scroll: [Data Compression](data-compression.pdf)
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* :scroll: [Data Compression](https://github.com/papers-we-love/papers-we-love/blob/master/data-compression/data-compression.pdf)
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> This paper surveys a variety of data compression methods spanning almost 40 years of research, from the work of Shannon, Fano and Huffman in the 40's, to a technique developed in 1986.
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*Ailon, Nir, and Bernard Chazelle. "The fast Johnson-Lindenstrauss transform and approximate nearest neighbors." SIAM Journal on Computing 39.1 (2009): 302-322. Available: https://www.cs.princeton.edu/~chazelle/pubs/FJLT-sicomp09.pdf*
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* [Renormalization](https://www.youtube.com/watch?v=_qjPFF5Gv1I) by Curt MacMullen
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Here is a video of a master (https://press.princeton.edu/titles/5669.html) talking about renormalisation. Which S Mallat has suggested is key to why deep learning works.
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* [Applications of Machine Learning to Location Data](http://www.berkkapicioglu.com/wp-content/uploads/2013/11/thesis_final.pdf)
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Using machine learning to design and analyze novel algorithms that leverage location data.
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* [Multiple Narrative Disentanglement: Unraveling *Infinite Jest*](http://dreammachin.es/p1-wallace.pdf)
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uses an unsupervised approach to natural language processing to classify narrators in David Foster Wallace's 1,000-page novel.
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Uses an unsupervised approach to natural language processing that classifies narrators in David Foster Wallace's 1,000-page novel.
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* [ImageNet Classification with Deep Convolutional Neural Networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
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*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*
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* :scroll: **[Truncation of Wavelet Matrices: Edge Effects and the Reduction of Topological Control](https://github.com/papers-we-love/papers-we-love/blob/master/machine_learning/Truncation-of-Wavelet-Matrices--Edge-Effects-and-Reduction-of-Topological-Control.pdf)** by Freedman
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In this paper by Michael Hartley Freedman, he applies Robion Kirby “torus trick”, via wavelets, to the problem of compression.
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In Simons Foundation’s interview by Michael Hartley Freedman of Robion Kirby, Freedman mentions this paper in which MHF applied RK’s “torus trick” to compression via wavelets.
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* :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
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* :scroll: **[Understanding Deep Convolutional Networks](https://github.com/papers-we-love/papers-we-love/blob/master/machine_learning/Understanding-Deep-Convolutional-Networks.pdf)** by Mallet
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Stéphane Mallat thinks renormalisation has something to do with why deep nets work.
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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.
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* :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
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Again on the topic of renormalisation. Dr Leinster has a nice, simple picture of self-similarity
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Dr Leinster's paper provides a concise, straightforward, picture of self-similarity, and its role in renormalization.
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## Mathematics
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* [:scroll:](transcendence-of-pi.pdf) [The Transcendence of pi](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/transcendence-of-pi.pdf) by Steve Mayer
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* [:scroll:] [Tilings](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/tilings.pdf) by Ardila
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* :scroll: [The Transcendence of pi](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/transcendence-of-pi.pdf) by Steve Mayer
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* :scroll: [Tilings](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/tilings.pdf) by Ardila
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This paper takes programmers out of the domain of what they are familair with counting, and into new terrain. The paper covers a broad swath of the topic of analysis of tiling, and related strategies.
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* [:scroll:] [From Dominoes to Hexagons](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/from-dominoes-to-hexagons.pdf) by Thurston
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* :scroll: [From Dominoes to Hexagons](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/from-dominoes-to-hexagons.pdf) by Thurston
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A paper on the generalization of tilings across different base planes.
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* [:scroll:] [graph isomorphism and representation theory](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/graph-isomorphism-and-representation-theory.pdf) by Daniel Litt
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* :scroll: [graph isomorphism and representation theory](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/graph-isomorphism-and-representation-theory.pdf) by Daniel Litt
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Programmers work with graphs often (file system, greplin, trees, "graph isomorphism problem"). But have you ever tried to construct a simpler building-block (basis) with which graphs could be built? Or at least a different building block to build the same old things.
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This <10 page paper also uses `𝔰𝔩₂(ℂ)` that will be seen to be a simple mathematical object, which leads into an area of real mathematics—rep theory.
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* [:scroll:] [Conway's ZIP proof](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/conways-zip-proof.pdf) by George Francis and Jeffrey Weeks
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* :scroll: [Conway's ZIP proof](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/conways-zip-proof.pdf) by George Francis and Jeffrey Weeks
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This paper is good for most knowledge levels because
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* it is pictorial
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* it is short
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* it is a classification proof: “How can it be that you know something about _all possible_ `X`, even the `xϵX` you haven’t seen yet? ”
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* [:scroll:] [packing of spheres](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/packing-of-spheres.pdf) by N. Sloane
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* :scroll: [packing of spheres](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/packing-of-spheres.pdf) by N. Sloane
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* The role of E8 & Leech lattice in optimal codes
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* An understanding of how mathematically-best compression was never used
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* Ikosahedrons
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* [:scroll:] [some underlying geometric notions](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/some-underlying-geometric-notions.pdf)
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* :scroll: [some underlying geometric notions](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/some-underlying-geometric-notions.pdf)
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This is a higher-level paper, but still a survey (so more readable). It ties together disparate areas like Platonic solids (A-D-E), Milnor’s exceptional fibre, and algebra.
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* [:scroll:] [what is a young tableaux?](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/what-is-a-young-tableau.pdf) by Alexander Yong
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* :scroll: [what is a young tableaux?](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/what-is-a-young-tableau.pdf) by Alexander Yong
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Young Tableau appear in many areas of mathematics. Beyond combinatoric problems, we also see them in representation theory, and the calculus of Grassmannians.
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### Topology
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* [:scroll:] [Topology of Numbers](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/topology-of-numbers--hatcher.pdf) by hatcher
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* :scroll: [Topology of Numbers](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/topology-of-numbers--hatcher.pdf) by hatcher
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* [Applied Algebraic Topology and Sensor Networks](https://www.math.upenn.edu/~ghrist/preprints/ATSN.pdf) by Robert Ghrist
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* [:scroll:] [Intro to Tropical Algebra Geometry](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/intro-to-tropical-algebraic-geometry.pdf)
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* :scroll: [Intro to Tropical Algebra Geometry](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/intro-to-tropical-algebraic-geometry.pdf)
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Recently there have been some papers posted about tropical geometry of neural nets. Tropical is also said to be derived from CS. This is a good introduction.
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* [:scroll:] [Elements of Algebraic Topology: Sheaves](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/elements-of-algebraic-topology-ch9-sheaves.pdf)
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* :scroll: [Elements of Algebraic Topology: Sheaves](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/elements-of-algebraic-topology-ch9-sheaves.pdf)
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Seminal writing on topological structures, from one most lauded books 'Elements of Algebraic Topology'
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