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diff --git a/pages/blog.org b/pages/blog.org
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-#+TITLE: Yuchen's Blog
-
-- 2019-03-14 - *[[file:posts/2019-03-14-great-but-manageable-expectations.org][Great but Manageable Expectations]]*
-- 2019-03-13 - *[[file:posts/2019-03-13-a-tail-of-two-densities.org][A Tail of Two Densities]]*
-- 2019-02-14 - *[[file:posts/2019-02-14-raise-your-elbo.org][Raise your ELBO]]*
-- 2019-01-03 - *[[file:posts/2019-01-03-discriminant-analysis.org][Discriminant analysis]]*
-- 2018-12-02 - *[[file:posts/2018-12-02-lime-shapley.org][Shapley, LIME and SHAP]]*
-- 2018-06-03 - *[[file:posts/2018-06-03-automatic_differentiation.org][Automatic differentiation]]*
-- 2018-04-29 - *[[file:posts/2018-04-10-update-open-research.org][Updates on open research]]*
-- 2017-08-07 - *[[file:posts/2017-08-07-mathematical_bazaar.org][The Mathematical Bazaar]]*
-- 2017-04-25 - *[[file:posts/2017-04-25-open_research_toywiki.org][Open mathematical research and launching toywiki]]*
-- 2016-10-13 - *[[file:posts/2016-10-13-q-robinson-schensted-knuth-polymer.org][A \(q\)-Robinson-Schensted-Knuth algorithm and a \(q\)-polymer]]*
-- 2015-07-15 - *[[file:posts/2015-07-15-double-macdonald-polynomials-macdonald-superpolynomials.org][AMS review of 'Double Macdonald polynomials as the stable limit of Macdonald superpolynomials' by Blondeau-Fournier, Lapointe and Mathieu]]*
-- 2015-07-01 - *[[file:posts/2015-07-01-causal-quantum-product-levy-area.org][On a causal quantum double product integral related to Lévy stochastic area.]]*
-- 2015-05-30 - *[[file:posts/2015-05-30-infinite-binary-words-containing-repetitions-odd-periods.org][AMS review of 'Infinite binary words containing repetitions of odd period' by Badkobeh and Crochemore]]*
-- 2015-04-02 - *[[file:posts/2015-04-02-juggling-skill-tree.org][jst]]*
-- 2015-04-01 - *[[file:posts/2015-04-01-unitary-double-products.org][Unitary causal quantum stochastic double products as universal]]*
-- 2015-01-20 - *[[file:posts/2015-01-20-weighted-interpretation-super-catalan-numbers.org][AMS review of 'A weighted interpretation for the super Catalan]]*
-- 2014-04-01 - *[[file:posts/2014-04-01-q-robinson-schensted-symmetry-paper.org][Symmetry property of \(q\)-weighted Robinson-Schensted algorithms and branching algorithms]]*
-- 2013-06-01 - *[[file:posts/2013-06-01-q-robinson-schensted-paper.org][A \(q\)-weighted Robinson-Schensted algorithm]]*
\ No newline at end of file
diff --git a/pages/microblog.org b/pages/microblog.org
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-#+TITLE: Yuchen's Microblog
-
-- *[[ia-lawsuit][2020-08-02]]* - ia lawsuit
-  <<ia-lawsuit>>
-  
-  The four big publishers Hachette, HarperCollins, Wiley, and Penguin
-  Random House are still pursuing Internet Archive.
-  
-  #+begin_quote
-  [Their] lawsuit does not stop at seeking to end the practice of
-  Controlled Digital Lending. These publishers call for the destruction
-  of the 1.5 million digital books that Internet Archive makes available
-  to our patrons. This form of digital book burning is unprecedented and
-  unfairly disadvantages people with print disabilities. For the blind,
-  ebooks are a lifeline, yet less than one in ten exists in accessible
-  formats. Since 2010, Internet Archive has made our lending library
-  available to the blind and print disabled community, in addition to
-  sighted users. If the publishers are successful with their lawsuit,
-  more than a million of those books would be deleted from the
-  Internet's digital shelves forever.
-  #+end_quote
-  
-  [[https://blog.archive.org/2020/07/29/internet-archive-responds-to-publishers-lawsuit/][Libraries
-  lend books, and must continue to lend books: Internet Archive responds
-  to publishers' lawsuit]]
-- *[[fsf-membership][2020-08-02]]* - fsf-membership
-  <<fsf-membership>>
-  
-  I am a proud associate member of Free Software Freedom. For me the
-  philosophy of Free Software is about ensuring the enrichment of a
-  digital commons, so that knowledge and information are not concentrated
-  in the hands of selected privileged people and locked up as
-  "intellectual property". The genius of copyleft licenses like GNU (A)GPL
-  ensures software released for the public, remains public. Open source
-  does not care about that.
-  
-  If you also care about the public good, the hacker ethics, or the spirit
-  of the web, please take a moment to consider joining FSF as an associate
-  member. It comes with [[https://www.fsf.org/associate/benefits][numerous
-  perks and benefits]].
-- *[[how-can-you-help-ia][2020-06-21]]* - how-can-you-help-ia
-  <<how-can-you-help-ia>>
-  
-  [[https://blog.archive.org/2020/06/14/how-can-you-help-the-internet-archive/][How
-  can you help the Internet Archive?]] Use it. It's more than the Wayback
-  Machine. And get involved.
-- *[[open-library][2020-06-12]]* - open-library
-  <<open-library>>
-  
-  Open Library was cofounded by Aaron Swartz. As part of the Internet
-  Archive, it has done good work to spread knowledge. However it is
-  currently
-  [[https://arstechnica.com/tech-policy/2020/06/internet-archive-ends-emergency-library-early-to-appease-publishers/][being
-  sued by four major publishers]] for the
-  [[https://archive.org/details/nationalemergencylibrary][National
-  Emergency Library]]. IA decided to
-  [[https://blog.archive.org/2020/06/10/temporary-national-emergency-library-to-close-2-weeks-early-returning-to-traditional-controlled-digital-lending/][close
-  the NEL two weeks earlier than planned]], but the lawsuit is not over,
-  which in the worst case scenario has the danger of resulting in
-  Controlled Digital Lending being considered illegal and (less likely)
-  bancruptcy of the Internet Archive. If this happens it will be a big
-  setback of the free-culture movement.
-- *[[sanders-suspend-campaign][2020-04-15]]* - sanders-suspend-campaign
-  <<sanders-suspend-campaign>>
-  
-  Suspending the campaign is different from dropping out of the race.
-  Bernie Sanders remains on the ballot, and indeed in his campaign
-  suspension speech he encouraged people to continue voting for him in the
-  democratic primaries to push for changes in the convention.
-- *[[defense-stallman][2019-09-30]]* - defense-stallman
-  <<defense-stallman>>
-  
-  Someone wrote a bold article titled
-  [[https://geoff.greer.fm/2019/09/30/in-defense-of-richard-stallman/]["In
-  Defense of Richard Stallman"]]. Kudos to him.
-  
-  Also, an interesting read:
-  [[https://cfenollosa.com/blog/famous-computer-public-figure-suffers-the-consequences-for-asshole-ish-behavior.html][Famous
-  public figure in tech suffers the consequences for asshole-ish
-  behavior]].
-- *[[stallman-resign][2019-09-29]]* - stallman-resign
-  <<stallman-resign>>
-  
-  Last week Richard Stallman resigned from FSF. It is a great loss for the
-  free software movement.
-  
-  The apparent cause of his resignation and the events that triggered it
-  reflect some alarming trends of the zeitgeist. Here is a detailed review
-  of what happened: [[https://sterling-archermedes.github.io/][Low grade
-  "journalists" and internet mob attack RMS with lies. In-depth review.]].
-  Some interesting articles on this are:
-  [[https://jackbaruth.com/?p=16779][Weekly Roundup: The Passion Of Saint
-  iGNUcius Edition]],
-  [[http://techrights.org/2019/09/17/rms-witch-hunt/][Why I Once Called
-  for Richard Stallman to Step Down]].
-  
-  Dishonest and misleading media pieces involved in this incident include
-  [[https://www.thedailybeast.com/famed-mit-computer-scientist-richard-stallman-defends-epstein-victims-were-entirely-willing][The
-  Daily Beast]],
-  [[https://www.vice.com/en_us/article/9ke3ke/famed-computer-scientist-richard-stallman-described-epstein-victims-as-entirely-willing][Vice]],
-  [[https://techcrunch.com/2019/09/16/computer-scientist-richard-stallman-who-defended-jeffrey-epstein-resigns-from-mit-csail-and-the-free-software-foundation/][Tech
-  Crunch]],
-  [[https://www.wired.com/story/richard-stallmans-exit-heralds-a-new-era-in-tech/][Wired]].
-- *[[decss-haiku][2019-03-16]]* - decss-haiku
-  <<decss-haiku>>
-  
-  #+begin_quote
-  #+begin_example
-    Muse!  When we learned to
-    count, little did we know all
-    the things we could do
-  
-    some day by shuffling
-    those numbers: Pythagoras
-    said "All is number"
-  
-    long before he saw
-    computers and their effects,
-    or what they could do
-  
-    by computation,
-    naive and mechanical
-    fast arithmetic.
-  
-    It changed the world, it
-    changed our consciousness and lives
-    to have such fast math
-  
-    available to
-    us and anyone who cared
-    to learn programming.
-  
-    Now help me, Muse, for
-    I wish to tell a piece of
-    controversial math,
-  
-    for which the lawyers
-    of DVD CCA
-    don't forbear to sue:
-  
-    that they alone should
-    know or have the right to teach
-    these skills and these rules.
-  
-    (Do they understand
-    the content, or is it just
-    the effects they see?)
-  
-    And all mathematics
-    is full of stories (just read
-    Eric Temple Bell);
-  
-    and CSS is
-    no exception to this rule.
-    Sing, Muse, decryption
-  
-    once secret, as all
-    knowledge, once unknown: how to
-    decrypt DVDs.
-  #+end_example
-  #+end_quote
-  
-  Seth Schoen, [[https://en.wikipedia.org/wiki/DeCSS_haiku][DeCSS haiku]]
-- *[[learning-undecidable][2019-01-27]]* - learning-undecidable
-  <<learning-undecidable>>
-  
-  My take on the
-  [[https://www.nature.com/articles/s42256-018-0002-3][Nature paper
-  /Learning can be undecidable/]]:
-  
-  Fantastic article, very clearly written.
-  
-  So it reduces a kind of learninability called estimating the maximum
-  (EMX) to the cardinality of real numbers which is undecidable.
-  
-  When it comes to the relation between EMX and the rest of machine
-  learning framework, the article mentions that EMX belongs to "extensions
-  of PAC learnability include Vapnik's statistical learning setting and
-  the equivalent general learning setting by Shalev-Shwartz and
-  colleagues" (I have no idea what these two things are), but it does not
-  say whether EMX is representative of or reduces to common learning
-  tasks. So it is not clear whether its undecidability applies to ML at
-  large.
-  
-  Another condition to the main theorem is the union bounded closure
-  assumption. It seems a reasonable property of a family of sets, but then
-  again I wonder how that translates to learning.
-  
-  The article says "By now, we know of quite a few independence [from
-  mathematical axioms] results, mostly for set theoretic questions like
-  the continuum hypothesis, but also for results in algebra, analysis,
-  infinite combinatorics and more. Machine learning, so far, has escaped
-  this fate." but the description of the EMX learnability makes it more
-  like a classical mathematical / theoretical computer science problem
-  rather than machine learning.
-  
-  An insightful conclusion: "How come learnability can neither be proved
-  nor refuted? A closer look reveals that the source of the problem is in
-  defining learnability as the existence of a learning function rather
-  than the existence of a learning algorithm. In contrast with the
-  existence of algorithms, the existence of functions over infinite
-  domains is a (logically) subtle issue."
-  
-  In relation to practical problems, it uses an example of ad targeting.
-  However, A lot is lost in translation from the main theorem to this ad
-  example.
-  
-  The EMX problem states: given a domain X, a distribution P over X which
-  is unknown, some samples from P, and a family of subsets of X called F,
-  find A in F that approximately maximises P(A).
-  
-  The undecidability rests on X being the continuous [0, 1] interval, and
-  from the insight, we know the problem comes from the cardinality of
-  subsets of the [0, 1] interval, which is "logically subtle".
-  
-  In the ad problem, the domain X is all potential visitors, which is
-  finite because there are finite number of people in the world. In this
-  case P is a categorical distribution over the 1..n where n is the
-  population of the world. One can have a good estimate of the parameters
-  of a categorical distribution by asking for sufficiently large number of
-  samples and computing the empirical distribution. Let's call the
-  estimated distribution Q. One can choose the from F (also finite) the
-  set that maximises Q(A) which will be a solution to EMX.
-  
-  In other words, the theorem states: EMX is undecidable because not all
-  EMX instances are decidable, because there are some nasty ones due to
-  infinities. That does not mean no EMX instance is decidable. And I think
-  the ad instance is decidable. Is there a learning task that actually
-  corresponds to an undecidable EMX instance? I don't know, but I will not
-  believe the result of this paper is useful until I see one.
-  
-  h/t Reynaldo Boulogne
-- *[[gavin-belson][2018-12-11]]* - gavin-belson
-  <<gavin-belson>>
-  
-  #+begin_quote
-  I don't know about you people, but I don't want to live in a world
-  where someone else makes the world a better place better than we do.
-  #+end_quote
-  
-  Gavin Belson, Silicon Valley S2E1.
-  
-  I came across this quote in
-  [[https://slate.com/business/2018/12/facebook-emails-lawsuit-embarrassing-mark-zuckerberg.html][a
-  Slate post about Facebook]]
-- *[[margins][2018-10-05]]* - margins
-  <<margins>>
-  
-  With Fermat's Library's new tool
-  [[https://fermatslibrary.com/margins][margins]], you can host your own
-  journal club.
-- *[[rnn-turing][2018-09-18]]* - rnn-turing
-  <<rnn-turing>>
-  
-  Just some non-rigorous guess / thought: Feedforward networks are like
-  combinatorial logic, and recurrent networks are like sequential logic
-  (e.g. data flip-flop is like the feedback connection in RNN). Since NAND
-  - combinatorial logic + sequential logic = von Neumann machine which is
-  an approximation of the Turing machine, it is not surprising that RNN
-  (with feedforward networks) is Turing complete (assuming that neural
-  networks can learn the NAND gate).
-- *[[zitierkartell][2018-09-07]]* - zitierkartell
-  <<zitierkartell>>
-  
-  [[https://academia.stackexchange.com/questions/116489/counter-strategy-against-group-that-repeatedly-does-strategic-self-citations-and][Counter
-  strategy against group that repeatedly does strategic self-citations and
-  ignores other relevant research]]
-- *[[short-science][2018-09-05]]* - short-science
-  <<short-science>>
-  
-  #+begin_quote
-  
-  
-  - ShortScience.org is a platform for post-publication discussion
-    aiming to improve accessibility and reproducibility of research
-    ideas.
-  - The website has over 800 summaries, mostly in machine learning,
-    written by the community and organized by paper, conference, and
-    year.
-  - Reading summaries of papers is useful to obtain the perspective and
-    insight of another reader, why they liked or disliked it, and their
-    attempt to demystify complicated sections.
-  - Also, writing summaries is a good exercise to understand the content
-    of a paper because you are forced to challenge your assumptions when
-    explaining it.
-  - Finally, you can keep up to date with the flood of research by
-    reading the latest summaries on our Twitter and Facebook pages.
-  #+end_quote
-  
-  [[https://shortscience.org][ShortScience.org]]
-- *[[darknet-diaries][2018-08-13]]* - darknet-diaries
-  <<darknet-diaries>>
-  
-  [[https://darknetdiaries.com][Darknet Diaries]] is a cool podcast.
-  According to its about page it covers "true stories from the dark side
-  of the Internet. Stories about hackers, defenders, threats, malware,
-  botnets, breaches, and privacy."
-- *[[coursera-basic-income][2018-06-20]]* - coursera-basic-income
-  <<coursera-basic-income>>
-  
-  Coursera is having
-  [[https://www.coursera.org/learn/exploring-basic-income-in-a-changing-economy][a
-  Teach-Out on Basic Income]].
-- *[[pun-generator][2018-06-19]]* - pun-generator
-  <<pun-generator>>
-  
-  [[https://en.wikipedia.org/wiki/Computational_humor#Pun_generation][Pun
-  generators exist]].
-- *[[hackers-excerpt][2018-06-15]]* - hackers-excerpt
-  <<hackers-excerpt>>
-  
-  #+begin_quote
-  But as more nontechnical people bought computers, the things that
-  impressed hackers were not as essential. While the programs themselves
-  had to maintain a certain standard of quality, it was quite possible
-  that the most exacting standards---those applied by a hacker who
-  wanted to add one more feature, or wouldn't let go of a project until
-  it was demonstrably faster than anything else around---were probably
-  counterproductive. What seemed more important was marketing. There
-  were plenty of brilliant programs which no one knew about. Sometimes
-  hackers would write programs and put them in the public domain, give
-  them away as easily as John Harris had lent his early copy of
-  Jawbreaker to the guys at the Fresno computer store. But rarely would
-  people ask for public domain programs by name: they wanted the ones
-  they saw advertised and discussed in magazines, demonstrated in
-  computer stores. It was not so important to have amazingly clever
-  algorithms. Users would put up with more commonplace ones.
-  
-  The Hacker Ethic, of course, held that every program should be as good
-  as you could make it (or better), infinitely flexible, admired for its
-  brilliance of concept and execution, and designed to extend the user's
-  powers. Selling computer programs like toothpaste was heresy. But it
-  was happening. Consider the prescription for success offered by one of
-  a panel of high-tech venture capitalists, gathered at a 1982 software
-  show: "I can summarize what it takes in three words: marketing,
-  marketing, marketing." When computers are sold like toasters, programs
-  will be sold like toothpaste. The Hacker Ethic notwithstanding.
-  #+end_quote
-  
-  [[http://www.stevenlevy.com/index.php/books/hackers][Hackers: Heroes of
-  Computer Revolution]], by Steven Levy.
-- *[[catalan-overflow][2018-06-11]]* - catalan-overflow
-  <<catalan-overflow>>
-  
-  To compute Catalan numbers without unnecessary overflow, use the
-  recurrence formula \(C_n = {4 n - 2 \over n + 1} C_{n - 1}\).
-- *[[boyer-moore][2018-06-04]]* - boyer-moore
-  <<boyer-moore>>
-  
-  The
-  [[https://en.wikipedia.org/wiki/Boyer–Moore_majority_vote_algorithm][Boyer-Moore
-  algorithm for finding the majority of a sequence of elements]] falls in
-  the category of "very clever algorithms".
-  
-  #+begin_example
-    int majorityElement(vector<int>& xs) {
-        int count = 0;
-        int maj = xs[0];
-        for (auto x : xs) {
-            if (x == maj) count++;
-            else if (count == 0) maj = x;
-            else count--;
-        }
-        return maj;
-    }
-  #+end_example
-- *[[how-to-learn-on-your-own][2018-05-30]]* - how-to-learn-on-your-own
-  <<how-to-learn-on-your-own>>
-  
-  Roger Grosse's post
-  [[https://metacademy.org/roadmaps/rgrosse/learn_on_your_own][How to
-  learn on your own (2015)]] is an excellent modern guide on how to learn
-  and research technical stuff (especially machine learning and maths) on
-  one's own.
-- *[[2048-mdp][2018-05-25]]* - 2048-mdp
-  <<2048-mdp>>
-  
-  [[http://jdlm.info/articles/2018/03/18/markov-decision-process-2048.html][This
-  post]] models 2048 as an MDP and solves it using policy iteration and
-  backward induction.
-- *[[ats][2018-05-22]]* - ats
-  <<ats>>
-  
-  #+begin_quote
-  ATS (Applied Type System) is a programming language designed to unify
-  programming with formal specification. ATS has support for combining
-  theorem proving with practical programming through the use of advanced
-  type systems. A past version of The Computer Language Benchmarks Game
-  has demonstrated that the performance of ATS is comparable to that of
-  the C and C++ programming languages. By using theorem proving and
-  strict type checking, the compiler can detect and prove that its
-  implemented functions are not susceptible to bugs such as division by
-  zero, memory leaks, buffer overflow, and other forms of memory
-  corruption by verifying pointer arithmetic and reference counting
-  before the program compiles. Additionally, by using the integrated
-  theorem-proving system of ATS (ATS/LF), the programmer may make use of
-  static constructs that are intertwined with the operative code to
-  prove that a function attains its specification.
-  #+end_quote
-  
-  [[https://en.wikipedia.org/wiki/ATS_(programming_language)][Wikipedia
-  entry on ATS]]
-- *[[bostoncalling][2018-05-20]]* - bostoncalling
-  <<bostoncalling>>
-  
-  (5-second fame) I sent a picture of my kitchen sink to BBC and got
-  mentioned in the [[https://www.bbc.co.uk/programmes/w3cswg8c][latest
-  Boston Calling episode]] (listen at 25:54).
-- *[[colah-blog][2018-05-18]]* - colah-blog
-  <<colah-blog>>
-  
-  [[https://colah.github.io/][colah's blog]] has a cool feature that
-  allows you to comment on any paragraph of a blog post. Here's an
-  [[https://colah.github.io/posts/2015-08-Understanding-LSTMs/][example]].
-  If it is doable on a static site hosted on Github pages, I suppose it
-  shouldn't be too hard to implement. This also seems to work more
-  seamlessly than [[https://fermatslibrary.com/][Fermat's Library]],
-  because the latter has to embed pdfs in webpages. Now fantasy time:
-  imagine that one day arXiv shows html versions of papers (through author
-  uploading or conversion from TeX) with this feature.
-- *[[random-forests][2018-05-15]]* - random-forests
-  <<random-forests>>
-  
-  [[https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/info][Stanford
-  Lagunita's statistical learning course]] has some excellent lectures on
-  random forests. It starts with explanations of decision trees, followed
-  by bagged trees and random forests, and ends with boosting. From these
-  lectures it seems that:
-  
-  1. The term "predictors" in statistical learning = "features" in machine
-    learning.
-  1. The main idea of random forests of dropping predictors for individual
-    trees and aggregate by majority or average is the same as the idea of
-    dropout in neural networks, where a proportion of neurons in the
-    hidden layers are dropped temporarily during different minibatches of
-    training, effectively averaging over an emsemble of subnetworks. Both
-    tricks are used as regularisations, i.e. to reduce the variance. The
-    only difference is: in random forests, all but a square root number
-    of the total number of features are dropped, whereas the dropout
-    ratio in neural networks is usually a half.
-  
-  By the way, here's a comparison between statistical learning and machine
-  learning from the slides of the Statistcal Learning course:
-- *[[open-review-net][2018-05-14]]* - open-review-net
-  <<open-review-net>>
-  
-  Open peer review means peer review process where communications
-  e.g. comments and responses are public.
-  
-  Like [[https://scipost.org/][SciPost]] mentioned in
-  [[file:/posts/2018-04-10-update-open-research.html][my post]],
-  [[https://openreview.net][OpenReview.net]] is an example of open peer
-  review in research. It looks like their focus is machine learning. Their
-  [[https://openreview.net/about][about page]] states their mission, and
-  here's [[https://openreview.net/group?id=ICLR.cc/2018/Conference][an
-  example]] where you can click on each entry to see what it is like. We
-  definitely need this in the maths research community.
-- *[[rnn-fsm][2018-05-11]]* - rnn-fsm
-  <<rnn-fsm>>
-  
-  Related to [[neural-turing-machine][a previous micropost]].
-  
-  [[http://www.cs.toronto.edu/~rgrosse/csc321/lec9.pdf][These slides from
-  Toronto]] are a nice introduction to RNN (recurrent neural network) from
-  a computational point of view. It states that RNN can simulate any FSM
-  (finite state machine, a.k.a. finite automata abbr. FA) with a toy
-  example computing the parity of a binary string.
-  
-  [[http://www.deeplearningbook.org/contents/rnn.html][Goodfellow et.
-  al.'s book]] (see page 372 and 374) goes one step further, stating that
-  RNN with a hidden-to-hidden layer can simulate Turing machines, and not
-  only that, but also the /universal/ Turing machine abbr. UTM (the book
-  referenced
-  [[https://www.sciencedirect.com/science/article/pii/S0022000085710136][Siegelmann-Sontag]]),
-  a property not shared by the weaker network where the hidden-to-hidden
-  layer is replaced by an output-to-hidden layer (page 376).
-  
-  By the way, the RNN with a hidden-to-hidden layer has the same
-  architecture as the so-called linear dynamical system mentioned in
-  [[https://www.coursera.org/learn/neural-networks/lecture/Fpa7y/modeling-sequences-a-brief-overview][Hinton's
-  video]].
-  
-  From what I have learned, the universality of RNN and feedforward
-  networks are therefore due to different arguments, the former coming
-  from Turing machines and the latter from an analytical view of
-  approximation by step functions.
-- *[[math-writing-decoupling][2018-05-10]]* - math-writing-decoupling
-  <<math-writing-decoupling>>
-  
-  One way to write readable mathematics is to decouple concepts. One idea
-  is the following template. First write a toy example with all the
-  important components present in this example, then analyse each
-  component individually and elaborate how (perhaps more complex)
-  variations of the component can extend the toy example and induce more
-  complex or powerful versions of the toy example. Through such
-  incremental development, one should be able to arrive at any result in
-  cutting edge research after a pleasant journey.
-  
-  It's a bit like the UNIX philosophy, where you have a basic system of
-  modules like IO, memory management, graphics etc, and modify / improve
-  each module individually (H/t [[http://nand2tetris.org/][NAND2Tetris]]).
-  
-  The book [[http://neuralnetworksanddeeplearning.com/][Neutral networks
-  and deep learning]] by Michael Nielsen is an example of such approach.
-  It begins the journey with a very simple neutral net with one hidden
-  layer, no regularisation, and sigmoid activations. It then analyses each
-  component including cost functions, the back propagation algorithm, the
-  activation functions, regularisation and the overall architecture (from
-  fully connected to CNN) individually and improve the toy example
-  incrementally. Over the course the accuracy of the example of mnist
-  grows incrementally from 95.42% to 99.67%.
-- *[[neural-nets-activation][2018-05-09]]* - neural-nets-activation
-  <<neural-nets-activation>>
-  
-  #+begin_quote
-  What makes the rectified linear activation function better than the
-  sigmoid or tanh functions? At present, we have a poor understanding of
-  the answer to this question. Indeed, rectified linear units have only
-  begun to be widely used in the past few years. The reason for that
-  recent adoption is empirical: a few people tried rectified linear
-  units, often on the basis of hunches or heuristic arguments. They got
-  good results classifying benchmark data sets, and the practice has
-  spread. In an ideal world we'd have a theory telling us which
-  activation function to pick for which application. But at present
-  we're a long way from such a world. I should not be at all surprised
-  if further major improvements can be obtained by an even better choice
-  of activation function. And I also expect that in coming decades a
-  powerful theory of activation functions will be developed. Today, we
-  still have to rely on poorly understood rules of thumb and experience.
-  #+end_quote
-  
-  Michael Nielsen,
-  [[http://neuralnetworksanddeeplearning.com/chap6.html#convolutional_neural_networks_in_practice][Neutral
-  networks and deep learning]]
-- *[[neural-turing-machine][2018-05-09]]* - neural-turing-machine
-  <<neural-turing-machine>>
-  
-  #+begin_quote
-  One way RNNs are currently being used is to connect neural networks
-  more closely to traditional ways of thinking about algorithms, ways of
-  thinking based on concepts such as Turing machines and (conventional)
-  programming languages. [[https://arxiv.org/abs/1410.4615][A 2014
-  paper]] developed an RNN which could take as input a
-  character-by-character description of a (very, very simple!) Python
-  program, and use that description to predict the output. Informally,
-  the network is learning to "understand" certain Python programs.
-  [[https://arxiv.org/abs/1410.5401][A second paper, also from 2014]],
-  used RNNs as a starting point to develop what they called a neural
-  Turing machine (NTM). This is a universal computer whose entire
-  structure can be trained using gradient descent. They trained their
-  NTM to infer algorithms for several simple problems, such as sorting
-  and copying.
-  
-  As it stands, these are extremely simple toy models. Learning to
-  execute the Python program =print(398345+42598)= doesn't make a
-  network into a full-fledged Python interpreter! It's not clear how
-  much further it will be possible to push the ideas. Still, the results
-  are intriguing. Historically, neural networks have done well at
-  pattern recognition problems where conventional algorithmic approaches
-  have trouble. Vice versa, conventional algorithmic approaches are good
-  at solving problems that neural nets aren't so good at. No-one today
-  implements a web server or a database program using a neural network!
-  It'd be great to develop unified models that integrate the strengths
-  of both neural networks and more traditional approaches to algorithms.
-  RNNs and ideas inspired by RNNs may help us do that.
-  #+end_quote
-  
-  Michael Nielsen,
-  [[http://neuralnetworksanddeeplearning.com/chap6.html#other_approaches_to_deep_neural_nets][Neural
-  networks and deep learning]]
-- *[[nlp-arxiv][2018-05-08]]* - nlp-arxiv
-  <<nlp-arxiv>>
-  
-  Primer Science is a tool by a startup called Primer that uses NLP to
-  summarize contents (but not single papers, yet) on arxiv. A developer of
-  this tool predicts in
-  [[https://twimlai.com/twiml-talk-136-taming-arxiv-w-natural-language-processing-with-john-bohannon/#][an
-  interview]] that progress on AI's ability to extract meanings from AI
-  research papers will be the biggest accelerant on AI research.
-- *[[neural-nets-regularization][2018-05-08]]* - neural-nets-regularization
-  <<neural-nets-regularization>>
-  
-  #+begin_quote
-  no-one has yet developed an entirely convincing theoretical
-  explanation for why regularization helps networks generalize. Indeed,
-  researchers continue to write papers where they try different
-  approaches to regularization, compare them to see which works better,
-  and attempt to understand why different approaches work better or
-  worse. And so you can view regularization as something of a kludge.
-  While it often helps, we don't have an entirely satisfactory
-  systematic understanding of what's going on, merely incomplete
-  heuristics and rules of thumb.
-  
-  There's a deeper set of issues here, issues which go to the heart of
-  science. It's the question of how we generalize. Regularization may
-  give us a computational magic wand that helps our networks generalize
-  better, but it doesn't give us a principled understanding of how
-  generalization works, nor of what the best approach is.
-  #+end_quote
-  
-  Michael Nielsen,
-  [[http://neuralnetworksanddeeplearning.com/chap3.html#why_does_regularization_help_reduce_overfitting][Neural
-  networks and deep learning]]
-- *[[sql-injection-video][2018-05-08]]* - sql-injection-video
-  <<sql-injection-video>>
-  
-  Computerphile has some brilliant educational videos on computer science,
-  like [[https://www.youtube.com/watch?v=ciNHn38EyRc][a demo of SQL
-  injection]], [[https://www.youtube.com/watch?v=eis11j_iGMs][a toy
-  example of the lambda calculus]], and
-  [[https://www.youtube.com/watch?v=9T8A89jgeTI][explaining the Y
-  combinator]].
-- *[[learning-knowledge-graph-reddit-journal-club][2018-05-07]]* - learning-knowledge-graph-reddit-journal-club
-  <<learning-knowledge-graph-reddit-journal-club>>
-  
-  It is a natural idea to look for ways to learn things like going through
-  a skill tree in a computer RPG.
-  
-  For example I made a
-  [[https://ypei.me/posts/2015-04-02-juggling-skill-tree.html][DAG for
-  juggling]].
-  
-  Websites like [[https://knowen.org][Knowen]] and
-  [[https://metacademy.org][Metacademy]] explore this idea with added
-  flavour of open collaboration.
-  
-  The design of Metacademy looks quite promising. It also has a nice
-  tagline: "your package manager for knowledge".
-  
-  There are so so many tools to assist learning / research / knowledge
-  sharing today, and we should keep experimenting, in the hope that
-  eventually one of them will scale.
-  
-  On another note, I often complain about the lack of a place to discuss
-  math research online, but today I found on Reddit some journal clubs on
-  machine learning:
-  [[https://www.reddit.com/r/MachineLearning/comments/8aluhs/d_machine_learning_wayr_what_are_you_reading_week/][1]],
-  [[https://www.reddit.com/r/MachineLearning/comments/8elmd8/d_anyone_having_trouble_reading_a_particular/][2]].
-  If only we had this for maths. On the other hand r/math does have some
-  interesting recurring threads as well:
-  [[https://www.reddit.com/r/math/wiki/everythingaboutx][Everything about
-  X]] and
-  [[https://www.reddit.com/r/math/search?q=what+are+you+working+on?+author:automoderator+&sort=new&restrict_sr=on&t=all][What
-  Are You Working On?]]. Hopefully these threads can last for years to
-  come.
-- *[[simple-solution-lack-of-math-rendering][2018-05-02]]* - simple-solution-lack-of-math-rendering
-  <<simple-solution-lack-of-math-rendering>>
-  
-  The lack of maths rendering in major online communication platforms like
-  instant messaging, email or Github has been a minor obsession of mine
-  for quite a while, as I saw it as a big factor preventing people from
-  talking more maths online. But today I realised this is totally a
-  non-issue. Just do what people on IRC have been doing since the
-  inception of the universe: use a (latex) pastebin.
-- *[[neural-networks-programming-paradigm][2018-05-01]]* - neural-networks-programming-paradigm
-  <<neural-networks-programming-paradigm>>
-  
-  #+begin_quote
-  Neural networks are one of the most beautiful programming paradigms
-  ever invented. In the conventional approach to programming, we tell
-  the computer what to do, breaking big problems up into many small,
-  precisely defined tasks that the computer can easily perform. By
-  contrast, in a neural network we don't tell the computer how to solve
-  our problem. Instead, it learns from observational data, figuring out
-  its own solution to the problem at hand.
-  #+end_quote
-  
-  Michael Nielsen -
-  [[http://neuralnetworksanddeeplearning.com/about.html][What this book
-  (Neural Networks and Deep Learning) is about]]
-  
-  Unrelated to the quote, note that Nielsen's book is licensed under
-  [[https://creativecommons.org/licenses/by-nc/3.0/deed.en_GB][CC BY-NC]],
-  so one can build on it and redistribute non-commercially.
-- *[[google-search-not-ai][2018-04-30]]* - google-search-not-ai
-  <<google-search-not-ai>>
-  
-  #+begin_quote
-  But, users have learned to accommodate to Google not the other way
-  around. We know what kinds of things we can type into Google and what
-  we can't and we keep our searches to things that Google is likely to
-  help with. We know we are looking for texts and not answers to start a
-  conversation with an entity that knows what we really need to talk
-  about. People learn from conversation and Google can't have one. It
-  can pretend to have one using Siri but really those conversations tend
-  to get tiresome when you are past asking about where to eat.
-  #+end_quote
-  
-  Roger Schank -
-  [[http://www.rogerschank.com/fraudulent-claims-made-by-IBM-about-Watson-and-AI][Fraudulent
-  claims made by IBM about Watson and AI]]
-- *[[hacker-ethics][2018-04-06]]* - hacker-ethics
-  <<hacker-ethics>>
-  
-  #+begin_quote
-  
-  
-  - Access to computers---and anything that might teach you something
-    about the way the world works---should be unlimited and total.
-    Always yield to the Hands-On Imperative!
-  - All information should be free.
-  - Mistrust Authority---Promote Decentralization.
-  - Hackers should be judged by their hacking, not bogus criteria such
-    as degrees, age, race, or position.
-  - You can create art and beauty on a computer.
-  - Computers can change your life for the better.
-  #+end_quote
-  
-  [[https://en.wikipedia.org/wiki/Hacker_ethic][The Hacker Ethic]],
-  [[https://en.wikipedia.org/wiki/Hackers:_Heroes_of_the_Computer_Revolution][Hackers:
-  Heroes of Computer Revolution]], by Steven Levy
-- *[[static-site-generator][2018-03-23]]* - static-site-generator
-  <<static-site-generator>>
-  
-  #+begin_quote
-  "Static site generators seem like music databases, in that everyone
-  eventually writes their own crappy one that just barely scratches the
-  itch they had (and I'm no exception)."
-  #+end_quote
-  
-  __david__@hackernews
-  
-  So did I.
\ No newline at end of file