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Articles related to "diverse"


'Hustlers' beat box office expectations with star power, social media, and inclusivity

  • Others, like Lizzo and Cardi B, grew to fame in part because of their social media presences, and still command highly engaged followings as a result.
  • The stars utilized those robust followings to promote the movie, posting pictures, clips, and eventually photos from the premier to encourage their followers to go see the flick.
  • Further, many of the movie's big names are stars in other mediums — like music — which could have helped the movie pull in viewers who don't usually buy movie tickets.
  • Movies like "Crazy Rich Asians," "Black Panther," "Moonlight," and "The Big Sick" featured casts representative of many specific demographics that wide-ranging crowds could connect with.
  • For instance, "The Big Sick," along the same lines as "Hustlers," had a teensy budget of just $5 million, but brought in 10 times that number in the box office.

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Scientists discover the structure of cancer molecule

  • One such process is alternative splicing, which gives cells access to a diverse range of proteins that originate from the same genetic source code but also serves different purposes within the cell, thereby ensuring its health.
  • However, when alternative splicing malfunctions, it can contribute to cancer's growth, spread, and ability to develop resistance to chemotherapy.
  • This is a molecule that plays an important role in alternative splicing, and its activity could help explain how cancer can hijack this vital process and use it for its own benefit.
  • DHX8 plays a role in the final step of splicing, in which genetic information is decoded, and it leads to the production of the diverse forms of protein.
  • Until now, scientists had a limited understanding of certain regions of DHX8's structure, including the "DEAH motif," the "hook loop," and the "hook turn." Now, however, the team has succeeded in uncovering more information about how they work.

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Diversity Sampling Cheatsheet

  • When you are building a Supervised Machine Learning model, you want to make sure that it covers as diverse a set of data and real-world demographics as possible.
  • The methods for ensuring that you have diverse training data for your model are a type of Active Learning called Diversity Sampling.
  • The recordings are predominantly from one gender and from people living at a small number of locations, meaning that resulting Machine Learning models are likely to be more accurate for that gender and only for some accents.
  • This is obviously a harder problem than simply knowing when your model is confused, which is why the solutions for Diversity Sampling are themselves more algorithmically diverse than those for Uncertainty Sampling.

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Diversity Sampling Cheatsheet

  • When you are building a Supervised Machine Learning model, you want to make sure that it covers as diverse a set of data and real-world demographics as possible.
  • The methods for ensuring that you have diverse training data for your model are a type of Active Learning called Diversity Sampling.
  • The recordings are predominantly from one gender and from people living at a small number of locations, meaning that resulting Machine Learning models are likely to be more accurate for that gender and only for some accents.
  • This is obviously a harder problem than simply knowing when your model is confused, which is why the solutions for Diversity Sampling are themselves more algorithmically diverse than those for Uncertainty Sampling.

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Diversity Sampling Cheatsheet

  • When you are building a Supervised Machine Learning model, you want to make sure that it covers as diverse a set of data and real-world demographics as possible.
  • The methods for ensuring that you have diverse training data for your model are a type of Active Learning called Diversity Sampling.
  • The recordings are predominantly from one gender and from people living at a small number of locations, meaning that resulting Machine Learning models are likely to be more accurate for that gender and only for some accents.
  • This is obviously a harder problem than simply knowing when your model is confused, which is why the solutions for Diversity Sampling are themselves more algorithmically diverse than those for Uncertainty Sampling.

save | comments | report | share on


Diversity Sampling Cheatsheet

  • When you are building a Supervised Machine Learning model, you want to make sure that it covers as diverse a set of data and real-world demographics as possible.
  • The methods for ensuring that you have diverse training data for your model are a type of Active Learning called Diversity Sampling.
  • The recordings are predominantly from one gender and from people living at a small number of locations, meaning that resulting Machine Learning models are likely to be more accurate for that gender and only for some accents.
  • This is obviously a harder problem than simply knowing when your model is confused, which is why the solutions for Diversity Sampling are themselves more algorithmically diverse than those for Uncertainty Sampling.

save | comments | report | share on