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[0] Loewenstein Y, Seung HS, Operant matching is a generic outcome of synaptic plasticity based on the covariance between reward and neural activity.Proc Natl Acad Sci U S A 103:41, 15224-9 (2006 Oct 10)

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ref: -0 tags: rutherford journal computational theory neumann complexity wolfram date: 05-05-2020 18:15 gmt revision:0 [head]

The Structures for Computation and the Mathematical Structure of Nature

  • Broad, long, historical.

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ref: Harris-1998.08 tags: noise wolpert harris motor planning Fitt velocity variance control theory date: 01-27-2013 22:33 gmt revision:1 [0] [head]

PMID-9723616[0] Signal-dependent noise determines motor planning.

  • We present a unifying theory of eye and arm movements based on the single physiological assumption that the neural control signals are corrupted by noise whose variance increases with the size of the control signal
    • Poisson noise? (I have not read the article -- storing here for future reference.)
  • This minimum-variance theory accurately predicts the trajectories of both saccades and arm movements and the speed-accuracy trade-off described by Fitt's law.

____References____

[0] Harris CM, Wolpert DM, Signal-dependent noise determines motor planning.Nature 394:6695, 780-4 (1998 Aug 20)

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ref: bookmark-0 tags: machine_learning research_blog parallel_computing bayes active_learning information_theory reinforcement_learning date: 12-31-2011 19:30 gmt revision:3 [2] [1] [0] [head]

hunch.net interesting posts:

  • debugging your brain - how to discover what you don't understand. a very intelligent viewpoint, worth rereading + the comments. look at the data, stupid
    • quote: how to represent the problem is perhaps even more important in research since human brains are not as adept as computers at shifting and using representations. Significant initial thought on how to represent a research problem is helpful. And when it’s not going well, changing representations can make a problem radically simpler.
  • automated labeling - great way to use a human 'oracle' to bootstrap us into good performance, esp. if the predictor can output a certainty value and hence ask the oracle all the 'tricky questions'.
  • The design of an optimal research environment
    • Quote: Machine learning is a victim of it’s common success. It’s hard to develop a learning algorithm which is substantially better than others. This means that anyone wanting to implement spam filtering can do so. Patents are useless here—you can’t patent an entire field (and even if you could it wouldn’t work).
  • More recently: http://hunch.net/?p=2016
    • Problem is that online course only imperfectly emulate the social environment of a college, which IMHO are useflu for cultivating diligence.
  • The unrealized potential of the research lab Quote: Muthu Muthukrishnan says “it’s the incentives”. In particular, people who invent something within a research lab have little personal incentive in seeing it’s potential realized so they fail to pursue it as vigorously as they might in a startup setting.
    • The motivation (money!) is just not there.

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ref: Loewenstein-2006.1 tags: reinforcement learning operant conditioning neural networks theory date: 12-07-2011 03:36 gmt revision:4 [3] [2] [1] [0] [head]

PMID-17008410[0] Operant matching is a generic outcome of synaptic plasticity based on the covariance between reward and neural activity

  • The probability of choosing an alternative in a long sequence of repeated choices is proportional to the total reward derived from that alternative, a phenomenon known as Herrnstein's matching law.
  • We hypothesize that there are forms of synaptic plasticity driven by the covariance between reward and neural activity and prove mathematically that matching (alternative to reward) is a generic outcome of such plasticity
    • models for learning that are based on the covariance between reward and choice are common in economics and are used phenomologically to explain human behavior.
  • this model can be tested experimentally by making reward contingent not on the choices, but rather on the activity of neural activity.
  • Maximization is shown to be a generic outcome of synaptic plasticity driven by the sum of the covariances between reward and all past neural activities.

____References____

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ref: work-0 tags: software development theory date: 10-26-2009 04:29 gmt revision:2 [1] [0] [head]

http://weblog.raganwald.com/2007/06/which-theory-first-evidence.html

  • Very good article, clearly the author has hard-earned experience..
    • I appreciate his (journalistic, correctful, maybe overbearing) tone, but personally think it much better to be a bit playful with the silly arbitrariness, imperfect-but-honestly-attempted decisions, that humans are.
  • One thing that I particularly liked was the idea of 'learning area' - the more competent people that you have working on a project and learning along the way, the more area is exposed to learning, facilitating progress. Compare to the top-down approach, which allocates a few very good people at the beginning of a project to plan it out, but then does not allow the implementors to modify the plan, and furthermore suggests mediocre implementors will do - all which minimizes the 'learning area'.

also from that site - http://weblog.raganwald.com/2007/05/not-so-big-software-application.html

  • The market for lemons, or "the bad driving out the good" - linked in the blog - brilliant!
  • Quote: "Adding detail makes a design more specific, but it only makes it specific for a client if the choices expressed address the most important needs of the client."

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ref: work-0 tags: Ng computational leaning theory machine date: 10-25-2009 19:14 gmt revision:0 [head]

Andrew Ng's notes on learning theory

  • goes over the bias / variance tradeoff.
    • variance = when the model has a large testing error; large generalization error.
    • bias = the expected generalization error even if the model is fit to a very large training set.
  • proves that, with a sufficiently large training set, the training error will be the same as the fitting error.
    • also gives an upper bound on the generalization error in terms of fitting error in terms of the number of models available (discrete number)
    • this bound is only logarithmic in k, the number of hypotheses.
  • the training size m that a certain method or algorithm requires in order to achieve a certain level of performance is the algorithm's sample complexity.
  • shows that with infinite hypothesis space, the number of training examples needed is at most linear in the parameters of the model.
  • goes over the Vapnik-Chervonenkis dimension = the size of the largest set that is shattered by a hypothesis space. = VC(H)
    • A hypothesis space can shatter a set if it can realize any labeling (binary, i think) on the set of points in S. see his diagram.
    • In oder to prove that VC(H) is at least D, only need to show that there's at least one set of size d that H can shatter.
  • There are more notes in the containing directory - http://www.stanford.edu/class/cs229/notes/

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ref: -0 tags: Vanity Fair American dream control theory in politics and society date: 05-03-2009 17:11 gmt revision:3 [2] [1] [0] [head]

Rethinking the American Dream by David Kamp

  • check out the lights in the frame at the bottom, and the kid taking a picture center-right (image courtesy of Kodak, hence.)

  • (quote:) "Still, we need to challenge some of the middle-class orthodoxies that have brought us to this point—not least the notion, widely promulgated throughout popular culture, that the middle class itself is a soul-suffocating dead end."
    • Perhaps they should teach expectations management in school? Sure, middle class should never die - I hope it will grow.
  • And yet, this is still rather depressive - we all want things to continuously, exponentially get better. I actually think this is almost possible, we just need to reason carefully about how this could happen: what changes in manufacturing, consumption, energy generation, transportation, and social organization would gradually effect widespread improvement.
    • Some time in individual lives (my own included!) is squandered in pursuit of the small pleasures which would be better used for purposeful endeavor. Seems we need to resurrect the idea of sacrifice towards the future (and it seems this meme itself is increasingly popular).
  • Realistically: nothing is for free; we are probably only enjoying this more recent economic boom because energy (and i mean oil, gas, coal, hydro, nuclear etc), which drives almost everything in society, is really cheap. If we can keep it this cheap, or make it cheaper through judicious investment in new technologies (and perhaps serendipity), then our standard of living can increase. That is not to say that it will - we need to put the caloric input to the economy to good use.
    • Currently our best system for enacting a general goal of efficiency is market-based capitalism. Now, the problem is that this is an inherently unstable system: there will be cheaters e.g. people who repackage crap mortgages as safe securities, companies who put lead paint on children's toys, companies who make unsafe products - and the capitalistic system, in and of itself, is imperfect at regulating these cheaters (*). Bureaucracy may not be the most efficient use of money or people's lives, but again it seems to be the best system for regulating/auditing cheaters. Examined from a control feedback point-of-view, bureaucracy 'tries' to control axes which pure capitalism does not directly address.
    • (*) Or is it? The largest problem with using consumer (or, more generally, individual) choice as the path to audit & evaluate production is that there is a large information gradient or knowledge difference between producers and consumers. It is the great (white?) hope of the internet generation that we can reduce this gradient, democratize information, and have everyone making better choices.
      • In this way, I'm very optimistic that things will get continuously better. (But recall that optimality-seeking requires time/money/energy - it ain't going to be free, and it certainly is not going to be 'natural'. Alternately, unstable-equilibrium-maintaining (servoing! auditing!) requires energy; democracy's big trick is that it takes advantage of a normal human behavior, bitching, as the feedstock. )
  • Finally (quote:) "I’m no champion of downward mobility, but the time has come to consider the idea of simple continuity: the perpetuation of a contented, sustainable middle-class way of life, where the standard of living remains happily constant from one generation to the next. "
    • Uh, you've had this coming: stick it. You can enjoy 'simple continuity'. My life is going to get better (or at least my life is going to change and be interesting/fun), and I expect the same for everybody else that I know. See logic above, and homoiconic's optimism

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ref: bookmark-0 tags: book information_theory machine_learning bayes probability neural_networks mackay date: 0-0-2007 0:0 revision:0 [head]

http://www.inference.phy.cam.ac.uk/mackay/itila/book.html -- free! (but i liked the book, so I bought it :)