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>Which is to say, effectively communicating something technical to a diverse audience is difficult, let's not be unnecessarily derisive. There's nothing especi

>Which is to say, effectively communicating something technical to a diverse audience is difficult, let's not be unnecessarily derisive. There's nothing especially derisive in my assessment. I don't think the content is bad, just boring. I also think it's too technical for a non-specialist audience. > Maybe you can do better as well? My first criticism is that generative models are not something specific to neural nets but that's not obvious from the article. My second criticism is that their explanations are overly mechanical. In the case of DCGAN the article begins by talking about parameters and magic numbers; i.e. they explain how the thing works rather than what it does, at an intuitive level. Clear enough?

platz 10y ago
Listen to gradstudent, he knows what he's talking about.. this article presents generative models as a grab bag of NN algorithms that came out this quarter.
- visarga 10y ago
  Most papers are migrating from plain classification to variational methods. This is the new trend. That means, instead of just predicting labels, they know also predict a probability for each label, a degree of confidence. And they work both ways, they can generate, as well as classify. And they are unsupervised, which means they can benefit from tons of data laying around.
  - platz 10y ago
    Your definition of "variational methods" is actually a definition of generative models. https://en.wikipedia.org/wiki/Generative_model https://en.wikipedia.org/wiki/Generative_model. variational methods is a much more specific concept, and your answer speaks more to the general and high-level concept of generative models. Notice on the wikipedia page for generative models, there is a lot more than variational methods.
visarga 10y ago
> I don't think the content is bad, just boring It's talking about machines gaining the power of imagination. How is that boring?
- gradstudent 10y ago
  > It's talking about machines gaining the power of imagination. How is that boring? It does nothing of the sort! If anyone comes away with this conclusion I would say the article has failed entirely. Which, btw, is my whole point: there's no over-arching intuitive explanation of what generative models are, why they're interesting or even what concrete problem they're solving.
choosername 10y ago
>they explain how the thing works rather than what it does In some kinds of logic this is impossible, but isn't that how reports are usually written? The what is the conclusion of the how. Many papers omit the mechanics completely and get critized for it, too.

platz 10y ago

Listen to gradstudent, he knows what he's talking about.. this article presents generative models as a grab bag of NN algorithms that came out this quarter.

visarga 10y ago
Most papers are migrating from plain classification to variational methods. This is the new trend. That means, instead of just predicting labels, they know also predict a probability for each label, a degree of confidence. And they work both ways, they can generate, as well as classify. And they are unsupervised, which means they can benefit from tons of data laying around.
- platz 10y ago
  Your definition of "variational methods" is actually a definition of generative models. https://en.wikipedia.org/wiki/Generative_model https://en.wikipedia.org/wiki/Generative_model. variational methods is a much more specific concept, and your answer speaks more to the general and high-level concept of generative models. Notice on the wikipedia page for generative models, there is a lot more than variational methods.

visarga 10y ago

> I don't think the content is bad, just boring It's talking about machines gaining the power of imagination. How is that boring?

gradstudent 10y ago
> It's talking about machines gaining the power of imagination. How is that boring? It does nothing of the sort! If anyone comes away with this conclusion I would say the article has failed entirely. Which, btw, is my whole point: there's no over-arching intuitive explanation of what generative models are, why they're interesting or even what concrete problem they're solving.

choosername 10y ago

>they explain how the thing works rather than what it does In some kinds of logic this is impossible, but isn't that how reports are usually written? The what is the conclusion of the how. Many papers omit the mechanics completely and get critized for it, too.