diff --git a/data_compression/README.md b/data_compression/README.md index a083cf9..a3686cf 100644 --- a/data_compression/README.md +++ b/data_compression/README.md @@ -1,4 +1,5 @@ # Data Compression -* :scroll: [Data Compression](data-compression.pdf) +* :scroll: [Data Compression](https://github.com/papers-we-love/papers-we-love/blob/master/data-compression/data-compression.pdf) + > 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. \ No newline at end of file diff --git a/machine_learning/General-self-similarity--an-overview.pdf b/machine_learning/General-self-similarity--an-overview.pdf new file mode 100644 index 0000000..2c11332 Binary files /dev/null and b/machine_learning/General-self-similarity--an-overview.pdf differ diff --git a/machine_learning/README.md b/machine_learning/README.md index 62f2e46..7afe3d3 100644 --- a/machine_learning/README.md +++ b/machine_learning/README.md @@ -2,28 +2,52 @@ ## External Papers -* [Top 10 algorithms in data mining](http://www.cs.uvm.edu/~icdm/algorithms/10Algorithms-08.pdf) - While it is difficult to identify the top 10, this paper contains 10 very important data mining/machine learning algorithms -* [A Few Useful Things to Know about Machine Learning](http://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf) - Just like the title says, it contains many useful tips and gotchas for machine learning -* [Random Forests](https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf) - The initial paper on random forests -* [Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data](http://repository.upenn.edu/cgi/viewcontent.cgi?article=1162&context=cis_papers) - The paper introducing conditional random fields as a framework for building probabilistic models. -* [Support-Vector Networks](http://rd.springer.com/content/pdf/10.1007%2FBF00994018.pdf) - The initial paper on support-vector networks for classification. +* [Top 10 algorithms in data mining](http://www.cs.uvm.edu/~icdm/algorithms/10Algorithms-08.pdf) + + While it is difficult to identify the top 10, this paper contains 10 very important data mining/machine learning algorithms + +* [A Few Useful Things to Know about Machine Learning](http://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf) + + Just like the title says, it contains many useful tips and gotchas for machine learning +* [Random Forests](https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf) + + The initial paper on random forests +* [Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data](http://repository.upenn.edu/cgi/viewcontent.cgi?article=1162&context=cis_papers) + + The paper introducing conditional random fields as a framework for building probabilistic models. +* [Support-Vector Networks](http://rd.springer.com/content/pdf/10.1007%2FBF00994018.pdf) + + The initial paper on support-vector networks for classification. + * [The Fast Johnson-Lindenstrauss Transforms](https://www.cs.princeton.edu/~chazelle/pubs/FJLT-sicomp09.pdf) The Johnson-Lindenstrauss transform (JLT) prescribes that there exists a matrix of size `k x d`, where `k = O(1/eps^2 log d)` such that with high probability, a matrix A drawn from this distribution preserves pairwise distances up to epsilon (e.g. `(1-eps) * ||x-y|| < ||Ax - Ay|| < (1+eps) ||x-y||`). This paper was the first paper to show that you can actually compute the JLT in less that `O(kd)` operations (e.g. you don't need to do the full matrix multiplication). They used their faster algorithm to construct one of the fastest known approximate nearest neighbor algorithms. *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* -* [Applications of Machine Learning to Location Data](http://www.berkkapicioglu.com/wp-content/uploads/2013/11/thesis_final.pdf) - Using machine learning to design and analyze novel algorithms that leverage location data. +* [Applications of Machine Learning to Location Data](http://www.berkkapicioglu.com/wp-content/uploads/2013/11/thesis_final.pdf) + + Using machine learning to design and analyze novel algorithms that leverage location data. -* ["Why Should I Trust You?" Explaining the Predictions of Any Classifier](http://www.kdd.org/kdd2016/papers/files/rfp0573-ribeiroA.pdf) - This paper introduces an explanation technique for any classifier in a interpretable manner. +* ["Why Should I Trust You?" Explaining the Predictions of Any Classifier](http://www.kdd.org/kdd2016/papers/files/rfp0573-ribeiroA.pdf) -* [Multiple Narrative Disentanglement: Unraveling *Infinite Jest*](http://dreammachin.es/p1-wallace.pdf) - uses an unsupervised approach to natural language processing to classify narrators in David Foster Wallace's 1,000-page novel. + This paper introduces an explanation technique for any classifier in a interpretable manner. -* [ImageNet Classification with Deep Convolutional Neural Networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) - This paper introduces AlexNet, a neural network architecture which dramatically improved over the state-of-the-art in image classification algorithms and is widely regarded as a breakthrough moment for deep learning. +* [Multiple Narrative Disentanglement: Unraveling *Infinite Jest*](http://dreammachin.es/p1-wallace.pdf) -* [Interpretable machine learning: definitions, methods, and applications](https://arxiv.org/pdf/1901.04592.pdf) - This paper introduces the foundations of the rapidly emerging field of interpretable machine learning. + Uses an unsupervised approach to natural language processing that classifies narrators in David Foster Wallace's 1,000-page novel. -* [Distilling the Knowledge in a Neural Network](https://arxiv.org/pdf/1503.02531.pdf) - This seminal paper introduces a method to distill information from an ensemble of neural networks into a single model. +* [ImageNet Classification with Deep Convolutional Neural Networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) + + This paper introduces AlexNet, a neural network architecture which dramatically improved over the state-of-the-art in image classification algorithms and is widely regarded as a breakthrough moment for deep learning. + +* [Interpretable machine learning: definitions, methods, and applications](https://arxiv.org/pdf/1901.04592.pdf) + + This paper introduces the foundations of the rapidly emerging field of interpretable machine learning. + +* [Distilling the Knowledge in a Neural Network](https://arxiv.org/pdf/1503.02531.pdf) + + This seminal paper introduces a method to distill information from an ensemble of neural networks into a single model. ## Hosted Papers @@ -39,3 +63,14 @@ *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 + + 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 + +Dr Leinster's paper provides a concise, straightforward, picture of self-similarity, and its role in renormalization. \ No newline at end of file diff --git a/machine_learning/Understanding-Deep-Convolutional-Networks.pdf b/machine_learning/Understanding-Deep-Convolutional-Networks.pdf new file mode 100644 index 0000000..0f4cbd8 Binary files /dev/null and b/machine_learning/Understanding-Deep-Convolutional-Networks.pdf differ diff --git a/mathematics/README.md b/mathematics/README.md index 515dc69..3030830 100644 --- a/mathematics/README.md +++ b/mathematics/README.md @@ -1,5 +1,45 @@ ## Mathematics -* [: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 +* :scroll: [The Transcendence of Pi](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/transcendence-of-pi.pdf) by Steve Mayer +* :scroll: [Tilings](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/tilings.pdf) by Ardila + The paper covers a broad swath of the topic on analysis of tiling, and related strategies. + +* :scroll: [From Dominoes to Hexagons](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/from-dominoes-to-hexagons.pdf) by Thurston + + A paper on the generalization of tilings across different base planes. + +* :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 + + The *graph isomorphism problem* shows how to construct graphs using a simple building-block ("basis"). The same method applies to finding different building blocks to construct the same things. This technique can be applied to file systems, greplin, trees, virtual DOM, etc. + + A short paper, it also shows how to use `𝔰𝔩₂(ℂ)` as a simple mathematical object that leads into the area of real mathematics—represention theory. + +* :scroll: [Conway's ZIP proof](https://www.maths.ed.ac.uk/~v1ranick/papers/francisweeks.pdf) by George Francis and Jeffrey Weeks + + This paper presents a classification proof: "How can it be that you know something about _all possible_ `X`, even the `xϵX` you haven’t seen yet?" The well-diagramed discussion requires no calculus, crypto, ML, or dense notation, making it good for most knowledge levels. + +* :scroll: [Packing of Spheres](http://neilsloane.com/doc/Me109.pdf) by N. Sloane + Discusses the role of E8 & Leech lattices in optimal codes for mathematically-ideal compression. Ikosahedrons, a tool in this investigation, are also presented. + +* :scroll: [Some Underlying Geometric Notions](https://pi.math.cornell.edu/~hatcher/AT/AT.pdf) by Hatcher + + High-Level survey which relates disparate topics, e.g. Platonic solids (A-D-E), Milnor’s exceptional fibre, and algebra. + +* :scroll: [What is a Young Tableaux?](https://www.ams.org/notices/200702/whatis-yong.pdf) by Alexander Yong + + Young Tableau appear in combinatoric problems, representation theory, and the calculus of Grassmannians. Another common topic is sorting, and smarter ways to organise sub-sorts. + + + +### Topology +* :scroll: [Topology of Numbers](https://pi.math.cornell.edu/~hatcher/TN/TNbook.pdf) by hatcher +* [Applied Algebraic Topology and Sensor Networks](https://www.math.upenn.edu/~ghrist/preprints/ATSN.pdf) by Robert Ghrist +* :scroll: [Intro to Tropical Algebra Geometry](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/intro-to-tropical-algebraic-geometry.pdf) + + 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. + +* :scroll: [Elements of Algebraic Topology: Sheaves](https://www.math.upenn.edu/~ghrist/EAT/EATchapter9.pdf) by Ghrist + + Seminal writing on topological structures, from one most lauded books 'Elements of Algebraic Topology' \ No newline at end of file diff --git a/mathematics/from-dominoes-to-hexagons.pdf b/mathematics/from-dominoes-to-hexagons.pdf new file mode 100644 index 0000000..93211f1 Binary files /dev/null and b/mathematics/from-dominoes-to-hexagons.pdf differ diff --git a/mathematics/graph-isomorphism-and-representation-theory.pdf b/mathematics/graph-isomorphism-and-representation-theory.pdf new file mode 100644 index 0000000..7595dd8 Binary files /dev/null and b/mathematics/graph-isomorphism-and-representation-theory.pdf differ diff --git a/mathematics/intro-to-tropical-algebraic-geometry.pdf b/mathematics/intro-to-tropical-algebraic-geometry.pdf new file mode 100644 index 0000000..12a347f Binary files /dev/null and b/mathematics/intro-to-tropical-algebraic-geometry.pdf differ diff --git a/mathematics/tilings.pdf b/mathematics/tilings.pdf new file mode 100644 index 0000000..b8a6033 Binary files /dev/null and b/mathematics/tilings.pdf differ