From 4c2bda91bc41676b406a8e4aa1d1f0af25dbe4d3 Mon Sep 17 00:00:00 2001 From: ZJ Date: Sun, 17 Nov 2019 17:03:42 -0500 Subject: [PATCH] added comentaries from commit messages. more consistent formatting. --- machine_learning/README.md | 56 ++++++++++++++++++++++++++++++-------- mathematics/README.md | 48 ++++++++++++++++++++++++++++++++ 2 files changed, 92 insertions(+), 12 deletions(-) diff --git a/machine_learning/README.md b/machine_learning/README.md index e7d2afa..7892bb7 100644 --- a/machine_learning/README.md +++ b/machine_learning/README.md @@ -2,11 +2,22 @@ ## 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. @@ -14,17 +25,32 @@ *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* * [Renormalization](https://www.youtube.com/watch?v=_qjPFF5Gv1I) by Curt MacMullen -* [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. + 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. -* [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. +* [Applications of Machine Learning to Location Data](http://www.berkkapicioglu.com/wp-content/uploads/2013/11/thesis_final.pdf) -* [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. + Using machine learning to design and analyze novel algorithms that leverage location data. -* [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. +* ["Why Should I Trust You?" Explaining the Predictions of Any Classifier](http://www.kdd.org/kdd2016/papers/files/rfp0573-ribeiroA.pdf) -* [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. + This paper introduces an explanation technique for any classifier in a interpretable manner. + +* [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. + +* [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 @@ -42,6 +68,12 @@ * :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 + 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. + * :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 -* :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 \ No newline at end of file + Stéphane Mallat thinks renormalisation has something to do with why deep nets work. + +* :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 + + Again on the topic of renormalisation. Dr Leinster has a nice, simple picture of self-similarity \ No newline at end of file diff --git a/mathematics/README.md b/mathematics/README.md index 515dc69..c7f0ddf 100644 --- a/mathematics/README.md +++ b/mathematics/README.md @@ -1,5 +1,53 @@ ## 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:] [Tilings](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/ardila.tilings.0501170.pdf) by Ardila + Programmers are used to counting boring things. Why not count something more interesting for a change? +* [:scroll:] [graph isomorphism and representation theory](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/daniel litt. graph isomorphism and representation theory. graphs-sl2.pdf) by Daniel Litt + + Programmers work with graphs often (file system, greplin, trees, "graph isomorphism problem" (who cares) ). 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. + + This <10-page paper also uses `𝔰𝔩₂(ℂ)`, a simple mathematical object you haven’t heard of, but which is a nice lead-in to an area of real mathematics—rep theory—that (1) contains actual insights (1a) that you aren’t using (2) is simple (3) isn’t pretentious. + +* [:scroll:] [Conway's ZIP proof](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/francis + weeks ZIP proof.pdf) by francis + weeks + + This paper can be shown to college freshmen because + * it’s pictorial + * it’s about an object you mightn’t have considered mathematical + * no calculus, crypto, ML, or pretentious notation + * it’s short + * it’s a classification proof: “How can it be that you know something about _all possible_ `X`, even the `xϵX` you haven’t seen yet?’ + +* [:scroll:] [from dominoes to hexagons](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/from-dominoes-to-hexagons.pdf) by Thurston +* [:scroll:] [On Invariants](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/OnOnInvariants.pdf) by Bar-Natan + + 2 pages about how notation and algorithms are inferior to clarity and simplicity. + +* [:scroll:] [packing of spheres](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/packing-of-spheres--sloane.pdf) by Sloane + * role of E8 & Leech lattice in optimal codes + * mathematically best compression was never used + * ikosahedron + +* [:scroll:] [some underlying geometric notions](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/some-underlying-geometric-notions.pdf) +* [:scroll:] [triality in so(4,4) characteristic classes, D4 G2 singularities](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/triality.in.so(4,4).characteristic.classes.d4.g2.singularities.1311.0507.pdf) by Mikosz and Weber + + 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. + + It has pictures and you’ll get a better sense of what mathematics is like from skimming it. + +* [:scroll:] [what is a young tableaux?](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/whatis a young tableau? alexander yong.pdf) by Alexander Yong + + You do sorting all the time. Are there smart ways to organise sub-sorts? + +### Topology +* [:scroll:] [topology of numbers](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/topology-of-numbers--hatcher.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-algebra-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:] [EAT: Sheaves](https://github.com/papers-we-love/papers-we-love/blob/master/mathematics/EAT-chapter9-sheaves.pdf) + + Varying your dimensionality across a space. But also — distributed robots! \ No newline at end of file