* [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
* [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.
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.
* [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.
* [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.
* ["Why Should I Trust You?" Explaining the Predictions of Any Classifier](http://www.kdd.org/kdd2016/papers/files/rfp0573-ribeiroA.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.
This paper introduces an explanation technique for any classifier in a interpretable manner.
* [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.
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
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
* [: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-pagepaperalsouses`𝔰𝔩₂(ℂ)`,asimplemathematicalobjectyouhaven’theardof,butwhichisanicelead-intoanareaofrealmathematics—reptheory—that(1)containsactualinsights(1a)thatyouaren’tusing(2)issimple(3)isn’tpretentious.
* [: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
* [: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.
ZJ
5 years ago