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I went through #638 and fixed the broken links in there. There was one I couldn't find, and it wasn't in the wayback machine so I deleted it.
80 lines
6.1 KiB
Markdown
80 lines
6.1 KiB
Markdown
# Machine Learning
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## External Papers
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* [Top 10 algorithms in data mining](https://www.researchgate.net/publication/29467751_Top_10_algorithms_in_data_mining)
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While it is difficult to identify the top 10, this paper contains 10 very important data mining/machine learning algorithms
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* [A Few Useful Things to Know about Machine Learning](http://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf)
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Just like the title says, it contains many useful tips and gotchas for machine learning
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* [Random Forests](https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf)
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The initial paper on random forests
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* [Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data](http://repository.upenn.edu/cgi/viewcontent.cgi?article=1162&context=cis_papers)
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The paper introducing conditional random fields as a framework for building probabilistic models.
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* [Support-Vector Networks](http://rd.springer.com/content/pdf/10.1007%2FBF00994018.pdf)
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The initial paper on support-vector networks for classification.
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* [The Fast Johnson-Lindenstrauss Transforms](https://www.cs.princeton.edu/~chazelle/pubs/FJLT-sicomp09.pdf)
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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.
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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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* [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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* ["Why Should I Trust You?" Explaining the Predictions of Any Classifier](http://www.kdd.org/kdd2016/papers/files/rfp0573-ribeiroA.pdf)
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This paper introduces an explanation technique for any classifier in a interpretable manner.
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* [Multiple Narrative Disentanglement: Unraveling *Infinite Jest*](http://aclanthology.org/N12-1001.pdf)
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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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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.
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* [Interpretable machine learning: definitions, methods, and applications](https://arxiv.org/pdf/1901.04592.pdf)
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This paper introduces the foundations of the rapidly emerging field of interpretable machine learning.
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* [Distilling the Knowledge in a Neural Network](https://arxiv.org/pdf/1503.02531.pdf)
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This seminal paper introduces a method to distill information from an ensemble of neural networks into a single model.
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* [Truncation of Wavelet Matrices: Edge Effects and the Reduction of Topological Control](https://reader.elsevier.com/reader/sd/pii/0024379594000395?token=EB0AA78D59A9648480596F018EFB72E0A02FD5FA70326B24B9D501E1A6869FE72CC4D97FA9ACC8BAB56060D6C908EC83) 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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## Hosted Papers
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* :scroll: **[A Sparse Johnson-Lindenstrauss Transform](dimensionality_reduction/a-sparse-johnson-lindenstrauss-transform.pdf)**
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The JLT is still computationally expensive for a lot of applications and one goal would be to minimize the overall operations needed to do the aforementioned matrix multiplication. This paper showed that a goal of a `O(k log d)` algorithm (e.g. `(log(d))^2)` may be attainable by showing that very sparse, structured random matrices could provide the *JL* guarantee on pairwise distances.
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*Dasgupta, Anirban, Ravi Kumar, and Tamás Sarlós. "A sparse johnson: Lindenstrauss transform." Proceedings of the forty-second ACM symposium on Theory of computing. ACM, 2010. Available: [arXiv/cs/1004:4240](http://arxiv.org/abs/1004.4240)*
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* :scroll: **[Towards a unified theory of sparse dimensionality reduction in Euclidean space](dimensionality_reduction/toward-a-unified-theory-of-sparse-dimensionality-reduction-in-euclidean-space.pdf)**
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This paper attempts to layout the generic mathematical framework (in terms of convex analysis and functional analysis) for sparse dimensionality reduction. The first author is a Fields Medalist who is interested in taking techniques for Banach Spaces and applying them to this problem. This paper is a very technical paper that attempts to answer the question, "when does a sparse embedding exist deterministically?" (e.g. doesn't require drawing random matrices).
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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: **[Understanding Deep Convolutional Networks](Understanding-Deep-Convolutional-Networks.pdf)** by Mallat
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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](General-self-similarity--an-overview.pdf)** by Leinster
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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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