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Anybody here on HN have experience with probabilistic-programming ? This looks quite disruptive if it works.
Really? "Disruptive"?
What's more interesting is that we're seeing heightened interest in these techniques again after they were ostensibly sidetracked in favor of statistical methods.
Can someone knowledgeable in graphics research explain the context that this question comes from?
If I am reading the question correctly, I infer that the question suggests that there exists a right way to reproduce the visual experience of reality. To me, this sounds like a question that is equally valid to have no answer (or many answers) in aesthetics, art, and philosophy, etc.
Earlier quoted context omitted.
Really? "Disruptive"?
Constraint solvers and similar tools can look pretty magical to people who use them for the first time, so I don't blame the OP. What's more interesting is that we're seeing heightened interest in these techniques again after they were ostensibly sidetracked in favor of statistical methods.
I'm not buying in just yet, because although it's nice to talk about model specification as completely independent processes, the availability of fast inference algorithms sometimes dictates what models you should choose. Sometimes less exact models with a larger parameter space that allows you to crunch orders of magnitude larger datasets (with approximate inference algorithms) yield more useful results than better specified models...and sometimes not. The thing if one still needs to know the whens and whys of picking certain models over others, and can't just gloss over the inference details.
> “It goes beyond image classification — the most popular task in computer vision — and tries to answer one of the most fundamental questions in computer vision: What is the right representation of visual scenes? Can someone knowledgeable in graphics research explain the context that this question comes from? If I am reading the question correctly, I infer that the question suggests that there exists a right way to r…
For example, if you can extract a 'Mesh' from a 2D picture, you can generate many other view points, and that mesh can be considered a good representation. If you are more sophisticated however (and perhaps have a larger "dictionary"), you can instead extract 'There are two wooden chairs 1m from each other, ...'.
That's the sense in which the representation is fundamental to computer vision -- it distills what the system knows (or what it wants to know) about scenes. The more concise the representation without loss of information the smarter your system is (and past a point becomes a general AI problem).
> “It goes beyond image classification — the most popular task in computer vision — and tries to answer one of the most fundamental questions in computer vision: What is the right representation of visual scenes? Can someone knowledgeable in graphics research explain the context that this question comes from? If I am reading the question correctly, I infer that the question suggests that there exists a right way to r…
See for example:
Natural Image Statistics — A probabilistic approach to early computational vision https://www.cs.helsinki.fi/u/ahyvarin/natimgsx/
Anybody here on HN have experience with probabilistic-programming ? This looks quite disruptive if it works.
For example, Stan-language supports MCMC modelling using Hamiltonian dynamics.
> “It goes beyond image classification — the most popular task in computer vision — and tries to answer one of the most fundamental questions in computer vision: What is the right representation of visual scenes? Can someone knowledgeable in graphics research explain the context that this question comes from? If I am reading the question correctly, I infer that the question suggests that there exists a right way to r…
Its Plato's Allegory of the Cave all the way down.
Imagine "watching" a movie compressed using your very own prior knowledge. Every scene could be described in couple of hundred lines of plaintext. Today we do this by reading a book :) What if we could build an algorithm able to render movies from books?
Is that 50 lines of code, or 50 lines of using a library that's thousands of lines of code?
I don't think libraries count in terms of code. We all use code to program. Standing on the shoulder that preceded us. Using a library and a function should just count for the most part.
Is that 50 lines of code, or 50 lines of using a library that's thousands of lines of code?
For a great example look at all the papers Andrew McCallum's group has been able to publish by building on top of FACTORIE: they get to focus their time on the problem at hand rather than all the math required to solve it. Basically they can write code that generates a model dynamically but the framework handles all the inference. Compare that to how these things are built without such a framework: you spend most of your time painstakingly hand-deriving update rules and then implementing them as code.
IMHO, the exciting thing is that ML is getting closer and closer to being an everyday tool for engineers rather than something that requires you to be a full-time math person to use effectively.