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| * [[https://openai.com/blog/adversarial-example-research/|Attacking machine learning with adversarial examples]]: Particular mention of image-classifying ANNs, which are especially prone to adversial noise that's imperceptible to humans. * [[https://hbr.org/2017/04/good-management-predicts-a-firms-success-better-than-it-rd-or-even-employee-skills|Good Management Predicts a Firm’s Success Better Than IT, R&D, or Even Employee Skills]]: An NBER study that appears to be done in R. * [[https://datawhatnow.com/simhash-question-deduplicatoin/|SimHash for question deduplication]]: Very easy intro to !SimHash. See also [[https://en.wikipedia.org/wiki/SimHash|the Wikipedia entry]]. |
Papers for discussion
An algorithm that finds truth even if most people are wrong [Prelec]: "Crowd" predictions are not necessarily good, but analyzing meta-knowledge of individual predictors can help you pick out the best predictors in the crowd.
Extracting the Wisdom of Crowds When Information is Shared [Palley]: Like Prelec's paper, but uses prediction of crowd's average as proxy for meta-knowledge, instead of prediction of crowd that would agree with you.
Clustering Similar Stories Using LDA: Good mash-up of ideas, including LDA (Latent Dirilecht Allocation), automatic dimensionality reduction, clustering.
Attacking machine learning with adversarial examples: Particular mention of image-classifying ANNs, which are especially prone to adversial noise that's imperceptible to humans.
Good Management Predicts a Firm’s Success Better Than IT, R&D, or Even Employee Skills: An NBER study that appears to be done in R.
SimHash for question deduplication: Very easy intro to SimHash. See also the Wikipedia entry.