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Articles related to "estimate"

Word2Vec Models Revisited

  • The embeddings of the four words were projected to four vectors using a shared weight matrix (and possibly a shared bias term), and the four vectors were averaged and the softmax were computed for the predicted word distribution.
  • The embedding of the input word were projected to one vector using a weight matrix (and possibly a bias term), the projected vector would be computed for softmax for the predicted word distribution.
  • Since computing $kP_n(w)$ usually only takes $O(1)$ constant time, and as the authors admitted it no longer approximates maximum likelihood estimation, probably this Negative Sampling should not exist at all in my opinion.
  • But since Negative Sampling no longer does maximum likelihood estimation, how could it still successfully train the word embeddings in the first place in the original paper?

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Presidential candidate Beto O’Rourke said he would take back assault weapons from Americans — here’s how a US government gun buyback might work

  • The earliest estimate we could find for the number of assault weapons in the United States comes from Mark Overstreet, a research coordinator at the National Rifle Association.
  • The 2015 National Firearms Survey, which drew on data collected from thousands of participants, placed the total American gun stock at 265 million weapons, and found that 33% of them were rifles, leading to an overall estimate of 87.4 million rifles.
  • Most voluntary buybacks run by city and state governments have opted for the fixed-price model: for example, in 2016 the Boston Police Department handed out $200 Target gift cards in exchange for returned firearms, and a Los Angeles buyback program implemented after the Sandy Hook Elementary School shooting offered $200 gift cards for returned assault weapons.
  • Combining the above estimates of the number of guns that would be banned and the possible cost per weapon, we were able to generate a range of estimates for the cost of a national buyback.

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