Twilight’s Kristen Stewart co-authored a paper on artificial intelligence

Kristen Stewart co-wrote an academic paper about artificial intelligence

  • Kristen Stewart â the actress best known for “Twilight” â has co-written a paper on machine learning.
  • Kristen Stewart – the actress best known…
  • According to the paper, the project was based on an impressionistic painting of Stewart’s, which shows a man waking up.
  • The response to the paper has been positive from the academic community, if slightly bemused.
  • The paper, first spotted by Quartz , is co-bylined with Adobe research engineer Bhautik J. Joshi and producer David Shapiro.

The actress and director outlined the use of neural style transfer in her film ‘Come Swim’.
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Kristen Stewart Publishes Research Paper on Using Artificial Intelligence to Create Art, Kicks Off Sundance Film Festival

Kristen Stewart publishes a research paper on using artificial intelligence to create art.

  • Kristen Stewart Publishes Research Paper on Using Artificial Intelligence to Create Art, Kicks Off Sundance Film Festival
  • Come Swim debuted at the Sundance Film Festival on Thursday.
  • The Twilight star co-authored a research paper, which published Wednesday on Cornell Univerity’s ArXiv , an online cache of non-peer reviewed research.
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  • If you’re into the heady science aspect of visual art, you can check out Stewart’s research .

The ‘Come Swim’ writer-director explores the intersection of science, technology and visual art in her newest film.
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GitHub

  • cifar-multi : Cifar10 classification using a convolutional neural network, where two independent learned optimizers are used.
  • learning_rate : Learning rate, only relevant if using Adam optimizer.
  • evaluation_period : Epochs before the optimizer is evaluated.
  • second_derivatives : If true , the optimizer will try to compute second derivatives through the loss function specified by the problem.
  • cifar : Cifar10 classification using a convolutional neural network.

learning-to-learn – Learning to Learn in TensorFlow
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Dmitriy Genzel’s answer to What is the difference between AI, Machine Learning, NLP, and Deep Learning?

#ArtificialIntelligence #MachineLearning #DeepLearning #NLP... what's the difference?

  • PhD in CS, Machine Learning Lead at Quora
  • Deep learning is one kind of machine learning that’s very popular now.
  • To draw a distinction with AI, if I can write a very clever program that has human-like behavior, it can be AI, but unless its parameters are automatically learned from data, it’s not machine learning.
  • It involves a particular kind of mathematical model that can be thought of as a composition of simple blocks (function composition) of a certain type, and where some of these blocks can be adjusted to better predict the final outcome.
  • AI ( Artificial intelligence ) is a subfield of computer science, that was created in the 1960s, and it was (is) concerned with solving tasks that are easy for humans, but hard for computers.

AI (Artificial intelligence) is a subfield of computer science, that was created in the 1960s, and it was (is) concerned with solving tasks that are easy for humans, but hard for computers. In particular, a so-called Strong AI would be a system that can do anything a human can (perhaps without purely physical things). This is fairly generic, and includes all kinds of tasks, such as planning, moving around in the world, recognizing objects and sounds, speaking, translating, performing social or business transactions, creative work (making art or poetry), etc.
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Dmitriy Genzel’s answer to What is the difference between AI, Machine Learning, NLP, and Deep Learning?

What is the difference between AI, ML, NLP, and Deep Learning? by Dmitriy Genzel, PhD in CS

  • Deep learning is one kind of machine learning that’s very popular now.
  • To draw a distinction with AI, if I can write a very clever program that has human-like behavior, it can be AI, but unless its parameters are automatically learned from data, it’s not machine learning.
  • It involves a particular kind of mathematical model that can be thought of as a composition of simple blocks (function composition) of a certain type, and where some of these blocks can be adjusted to better predict the final outcome.
  • AI ( Artificial intelligence ) is a subfield of computer science, that was created in the 1960s, and it was (is) concerned with solving tasks that are easy for humans, but hard for computers.
  • Submit any pending changes before refreshing this page.

AI (Artificial intelligence) is a subfield of computer science, that was created in the 1960s, and it was (is) concerned with solving tasks that are easy for humans, but hard for computers. In particular, a so-called Strong AI would be a system that can do anything a human can (perhaps without purely physical things). This is fairly generic, and includes all kinds of tasks, such as planning, moving around in the world, recognizing objects and sounds, speaking, translating, performing social or business transactions, creative work (making art or poetry), etc.
Continue reading “Dmitriy Genzel’s answer to What is the difference between AI, Machine Learning, NLP, and Deep Learning?”

[1605.06465] Swapout: Learning an ensemble of deep architectures

Swapout:Learning ensemble of deep architectures (dropout/Res/Stoch. depth)  #deeplearning #ML

  • When viewed as an ensemble training method, it samples a much richer set of architectures than existing methods such as dropout or stochastic depth.
  • We propose a parameterization that reveals connections to exiting architectures and suggests a much richer set of architectures to be explored.
  • Swapout samples from a rich set of architectures including dropout, stochastic depth and residual architectures as special cases.
  • When viewed as a regularization method swapout not only inhibits co-adaptation of units in a layer, similar to dropout, but also across network layers.
  • We conjecture that swapout achieves strong regularization by implicitly tying the parameters across layers.

Read the full article, click here.


@Deep_Hub: “Swapout:Learning ensemble of deep architectures (dropout/Res/Stoch. depth) #deeplearning #ML”



[1605.06465] Swapout: Learning an ensemble of deep architectures