We care deeply about privacy. To strengthen our knowledge in this field and understand how it relates to visual data, we do constant research in the latest scientific works about this topic. We want to share with you a curated list of machine/deep learning papers that address the topic of privacy in visual data. Although we know that it is very specific field, we think it is important to raise awareness about it.
I organised link according to this discussion https://github.com/papers-we-love/papers-we-love/issues/610
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* From where it all started - Sun NFS
* Updated links of papers
* Does not fit in this category
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* Already exists.
* Reviewed for PR
* Original link updated
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* Absolute links updated
* added some literatures covering scientific data compression.
* fixed the 📜 notation in readme file
* added the urls to the paper in readme file; added reasons for the paper in readme file.
* fixed the name of the paper in readme
Co-authored-by: Sean Broderick <hakutsuru@mac.com>
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* Rename security/HW Security/README.md to security/hardware_security/README.md
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* Delete sok-eternal-war-in-memory.pdf
* Moving sok-eternal-war-in-memory.pdf
* Fix dead link
Updating the link for "Internet Census via Insecure Routers"
* Add Hardware Security subsection
* Add Bioinformatics research area
* fix the Shouji paper link
* Added script to download all PDFs from the Readmes
* Removed sleep
* Formatting
* Added guard closes and some docs to download script. Added it to scripts folder. Added download script readme. Added section in root readme.
* Removed old download_all.sh
* Added support for specifying which directories you want to download.
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* Changed filename to download.sh. Updated READMEs.
* More README
* Fixed download.sh logic for multiple arguments. Removed Readme section about executing script from anywhere. Updated the parsing of URLs to be more specific.
* Create README.md
* Rename security/HW Security/README.md to security/hardware_security/README.md
* Rearranging files
* Rearranging files
* Delete sok-eternal-war-in-memory.pdf
* Moving sok-eternal-war-in-memory.pdf
* Fix dead link
Updating the link for "Internet Census via Insecure Routers"
* Add Hardware Security subsection
* Add gitter for community.
* Update CODE_OF_CONDUCT.md
* Add statecharts paper in a new systems modeling category (#565)
* Rename "paradigm" and "plt" folders for findability (#561)
* rename "language-paradigm" folder for findability
lang para pluralize
* rename PLT => languages-theory
* fixed formatting
* group pattern-* related papers (#564)
* combine clustering algo into pattern matching
* rename stringology with the pattern_ prefix
* improved the README header info for paper related to patterns
* consolidate org-sim and sw-eng dirs (#567)
* consolidate org-sim and sw-eng dirs
* typo and links
* Fixed link (#568)
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* Fixed A Unified Theory of Garbage Collection link
* Verification faults dirs (#566)
* consolidate program verificaiton and program fault detection listings.
* faults and validation gets header info
* self-similarity by Tom Leinster
Again on the topic of renormalisation. Dr Leinster has a nice, simple picture of self-similarity.
* added new papers in Machine Learning dir. fixed-up references
Truncation of Wavelet Matrices
Understanding Deep Convolutional Networks
General self-similarity: an overview
cleanup url files (wrong repo format)
* what has sphere packing to do with compression?
• role of E8 & Leech lattice in optimal codes
• mathematically best compression was never used
• ikosahedron
* surfaces ∑
I show this paper 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?’
* good combinatorics
Programmers are used to counting boring things. Why not count something more interesting for a change?
* added comentaries from commit messages. more consistent formatting.
* graphs
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.
* from dominoes to hexagons
why is this super-smart guy interested in such simple drawings?
* sorting
You do sorting all the time. Are there smart ways to organise sub-sorts?
* distributed robots!!
Robots! And varying your dimensionality across a space. But also — distributed robots!
* knitting
Get into knitting.
Learn a data structure that needs to be embedded in 3D to do its thing.
Break your mind a bit.
* female genius
* On “On Invariants of Manifolds”
2 pages about how notation and algorithms are inferior to clarity and simplicity.
* pretty robots
You’ll understand calculus better after looking at these pretty 75 pages.
* Farey
Have another look at ye olde Int class.
* renormalisation
Stéphane Mallat thinks renormalisation has something to do with why deep nets work.
* the torus trick, applied
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.
* renormalisation
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.
* Cartan triality + Milnor fibre
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.
* Create see.machine.learning
* tropical geometry
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.
* self-similarity by Tom Leinster
Again on the topic of renormalisation. Dr Leinster has a nice, simple picture of self-similarity.
* rename papers accordingly, and add descriptive info
remove dup maths papers
* fixed crappy explanations
* improved the annotations for papers in the Machine Learning readme
* remediated descriptive wording for papers in the mathematics section
* removed local copy and added link to Conway Zip Proof
* removed local copy and added link to Packing of Spheres - Sloane
* removed local copy and added link to Algebraic Topo - Hatcher
* removed local copy and added link to Topo of Numbers - Hatcher
* removed local copy and added link to Young Tableax - Yong
* removed local copy and added link to Elements of A Topo
* removed local copy and added link to Truncation of Wavlet Matrices
Co-authored-by: Zeeshan Lakhani <202820+zeeshanlakhani@users.noreply.github.com>
Co-authored-by: Wiktor Czajkowski <wiktor.czajkowski@gmail.com>
Co-authored-by: keddad <keddad@yandex.ru>
Co-authored-by: i <isomorphisms@sdf.org>
* created ReadMe
* Update and rename Brain–computer Interface/README.md to Brain-computer Interface/README.md
* Updating Readme as per @hakutsuru's review
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* Adding Object Identification for Computer Vision Using Image Segmentation and Computer Vision Based Detection and Localization of Potholes in Asphalt Pavement Images
* Adding Object Identification for Computer Vision Using Image Segmentation and Computer Vision Based Detection and Localization of Potholes in Asphalt Pavement Images
* combine clustering algo into pattern matching
* rename stringology with the pattern_ prefix
* improved the README header info for paper related to patterns
