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* 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) * Update README.md * 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> |
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| affective-computing | ||
| android | ||
| api_design | ||
| artificial_intelligence | ||
| audio_comp_sci | ||
| biocomputing | ||
| brain-computer-interface | ||
| caching | ||
| combinatory_logic | ||
| comp_sci_fundamentals_and_history | ||
| computational_creativity | ||
| computer_architecture | ||
| computer_education | ||
| computer_graphics | ||
| computer_vision | ||
| concurrency | ||
| crash_only | ||
| cryptography | ||
| data_compression | ||
| data_fusion | ||
| data_replication | ||
| data_science | ||
| data_structures | ||
| datastores | ||
| design | ||
| digital_currency | ||
| distributed_systems | ||
| economics | ||
| ethics | ||
| experimental_algorithmics | ||
| faults_and_verification | ||
| gamification | ||
| garbage_collection | ||
| gossip | ||
| information_retrieval | ||
| information_theory | ||
| languages | ||
| languages-paradigms | ||
| languages-theory | ||
| logic_and_programming | ||
| machine_learning | ||
| macros | ||
| mathematics | ||
| memory_management | ||
| networks | ||
| non_blocking_algorithms | ||
| operating_systems | ||
| pattern_matching | ||
| pattern_stringology | ||
| physics | ||
| privacy | ||
| processes | ||
| quantum_computing | ||
| robotics | ||
| security | ||
| software_engineering_orgs | ||
| speech_recognition | ||
| sports_analytics | ||
| streaming_algorithms | ||
| sublinear_algorithms | ||
| systematic_review | ||
| systems_modeling | ||
| testing | ||
| time_series | ||
| unikernels | ||
| user_interfaces | ||
| virtual_machines | ||
| .gitignore | ||
| CODE_OF_CONDUCT.md | ||
| nautilus.db | ||
| README.md | ||
Papers We Love (PWL) is a community built around reading, discussing and learning more about academic computer science papers. This repository serves as a directory of some of the best papers the community can find, bringing together documents scattered across the web. You can also visit the Papers We Love site for more info.
Due to licenses we cannot always host the papers themselves (when we do, you will see a 📜 emoji next to its title in the directory README) but we can provide links to their locations.
If you enjoy the papers, perhaps stop by a local chapter meetup and join in on the vibrant discussions around them. You can also discuss PWL events, the content in this repository, and/or anything related to PWL on our Slack, after signing-up to join it, or on our #paperswelove IRC channel on freenode.
Chapters
Here are our official chapters. Let us know if you are interested in starting one in your city!
- Amsterdam
- Athens
- Atlanta
- Bangalore
- Barcelona
- Beijing
- Belfast
- Berlin
- Bhubaneswar
- Boston
- Brasilia
- Boulder
- Bucharest
- Buenos Aires
- Cairo
- Chattanooga
- Chicago
- Columbus, Ohio
- Dallas
- Gothenburg
- Guadalajara
- Hamburg
- Hyderabad
- Iasi
- Iowa City
- Kathmandu
- Kyiv
- Lebanon
- London
- Los Angeles
- Madrid
- Medellín
- Montreal
- Mumbai
- Munich
- New York City
- Paris
- Philadelphia
- Portland
- Porto
- Pune
- Raleigh-Durham
- Reykjavík
- Rio de Janeiro
- San Diego
- San Francisco
- Seattle
- Seoul, Korea
- Singapore
- St. Louis
- Teresina
- Toronto
- Vienna
- Washington, DC
- Winnipeg
- Zürich
All of our meetups follow our Code of Conduct.
Past Presentations
Check out our YouTube and MixCloud (audio-only format) channels.
Info
We're looking for pull requests related to papers we should add, better organization of the papers we do have, and/or links to other paper-repos we should point to.
Other Good Places to Find Papers
- 2 Minute Papers
- Bell System Technical Journal, 1922-1983
- Best Paper Awards in Computer Science
- Google Scholar (choose a subcategory)
- Microsoft Research
- Functional Programming Books Review
- MIT's Artificial Intelligence Lab Publications
- MIT's Distributed System's Reading Group
- arXiv Paper Repository
- SciRate
- cat-v.org
- y-archive
- netlib
- Services Engineering Reading List
- Readings in Distributed Systems
- Gradual Typing Bibliography
- Security Data Science Papers
- Research Papers from Robert Harper, Carnegie Mellon University
- Lobste.rs tagged as PDF
- The Morning Paper
Please check out our wiki-page for links to blogs, books, exchanges that are worth a good read.
How To Read a Paper
Reading a paper is not the same as reading a blogpost or a novel. Here are a few handy resources to help you get started.
- How to read an academic article
- Advice on reading academic papers
- How to read and understand a scientific paper
- Should I Read Papers?
- The Refreshingly Rewarding Realm of Research Papers
Applications/Ideas built around Papers We Love
- Love a Paper - @loveapaper
Contributing Guidelines
Please take a look at our CONTRIBUTING.md file.
Copyright
The name "Papers We Love" and the logos for the organization are copyrighted, and under the ownership of Papers We Love Ltd, all rights reserved. When starting a chapter, please review our guidelines and ask us about using the logo.