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authorYuchen Pei <me@ypei.me>2021-07-01 15:26:34 +1000
committerYuchen Pei <me@ypei.me>2021-07-01 15:26:34 +1000
commitb0c5898aa4f97ac62f10eead1c263a6902425392 (patch)
tree641dac3ec16a778fee5a7fe251b4e658ddc40dfb /pages
parentbd3b4e7d8a436685f8b676da8f6ffe9498ab2e3f (diff)
minor changes and removed some unused assets.
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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
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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