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[0] Narayanan NS, Kimchi EY, Laubach M, Redundancy and synergy of neuronal ensembles in motor cortex.J Neurosci 25:17, 4207-16 (2005 Apr 27)

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ref: notes-0 tags: data effectiveness Norvig google statistics machine learning date: 12-06-2011 07:15 gmt revision:1 [0] [head]

The unreasonable effectiveness of data.

  • counterpoint to Eugene Wigner's "The Unreasonable effectiveness of mathematics in the natural sciences"
    • that is, math is not effective with people.
    • we should not look for elegant theories, rather embrace complexity and make use of extensive data. (google's mantra!!)
  • in 2006 google released a trillion-word corpus with all words up to 5 words long.
  • document translation and voice transcription are successful mostly because people need the services - there is demand.
    • Traditional natural language processing does not have such demand as of yet. Furthermore, it has required human-annotated data, which is expensive to produce.
  • simple models and a lot of data triumph more elaborate models based on less data.
    • for translation and any other application of ML to web data, n-gram models or linear classifiers work better than elaborate models that try to discover general rules.
  • much web data consists of individually rare but collectively frequent events.
  • because of a huge shared cognitive and cultural context, linguistic expression can be highly ambiguous and still often be understood correctly.
  • mention project halo - $10,000 per page of a chemistry textbook. (funded by DARPA)
  • ultimately suggest that there is so so much to explore now - just use unlabeled data with an unsupervised learning algorithm.

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ref: work-0 tags: covariance matrix adaptation learning evolution continuous function normal gaussian statistics date: 06-30-2009 15:07 gmt revision:0 [head]

http://www.lri.fr/~hansen/cmatutorial.pdf

  • Details a method of sampling + covariance matrix approximation to find the extrema of a continuous (but intractable) fitness function
  • HAs flavors of RLS / Kalman filtering. Indeed, i think that kalman filtering may be a more principled method for optimization?
  • Can be used in high-dimensional optimization problems like finding optimal weights for a neural network.
  • Optimum-seeking is provided by weighting the stochastic samples (generated ala a particle filter or unscented kalman filter) by their fitness.
  • Introductory material is quite good, actually...

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ref: Narayanan-2005.04 tags: Laubach M1 motor rats statistics BMI prediction methods date: 09-07-2008 19:51 gmt revision:4 [3] [2] [1] [0] [head]

PMID-15858046[] Redundancy and Synergy of Neuronal Ensembles in Motor Cortex

  • timing task.
  • rats.
  • 50um teflon microwires in motor cortex
  • ohno : neurons that were the best predictors of task performance were not necessarily the neurons that contributed the most predictive information to an ensemble of neurons.
  • most all contribute redundant predictive information to the ensemble.
    • this redundancy kept the predictions high, even if neurons were dropped.
  • small groups of neurons were more synergistic
  • large groups were more redundant.
  • used wavelet based discriminant pursuit.
    • validated with draws from a random data set.
  • used R and Weka
  • data looks hella noisy ?

____References____

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ref: bookmark-0 tags: statistics logistic regression binomial logit BIC AIC SPSS date: 0-0-2006 0:0 revision:0 [head]

http://www2.chass.ncsu.edu/garson/PA765/logistic.htm

  • transform probabilities into logarithmic variables = logits