If you're going to do a "rebrand" atleast use a better name. A six syllable word doesn't exactly roll off the tongue.
differentiable.js.io
“Deep Learning has outlived its usefulness as a buzz-phrase”
51–60 of 107 posts
Re: “Deep Learning has outlived its usefulness as a buzz-phrase”
#52I wish we could come up with a catchier name, but I LOVE the idea of calling this programming , because that is precisely what we do when we compose deep neural nets. For example, here's how you compose a neural net consisting of two "dense" layers (linear transformations), using Keras's functional API, and then apply these two layers to some tensor x to obtain a tensor y: f = Dense(n) g = Dense(n) y = f(g(x)) This l…
I hate the name, but LOVE the idea of calling this programming...
Re: “Deep Learning has outlived its usefulness as a buzz-phrase”
#53[Text from post] OK, Deep Learning has outlived its usefulness as a buzz-phrase. Deep Learning est mort. Vive Differentiable Programming! Yeah, Differentiable Programming is little more than a rebranding of the modern collection Deep Learning techniques, the same way Deep Learning was a rebranding of the modern incarnations of neural nets with more than two layers. But the important point is that people are now build…
it's really a pity that after 75 years of AI research the best thing we've got is still based on gradient descent, a brute force trial and error.
Assuming that AI tries to mimic the way humans learn and evolve, those methods haven't changed for hundreds of thousands of years and brute-force trial and error is just one of them. It's kind of fundamental...
Re: “Deep Learning has outlived its usefulness as a buzz-phrase”
#54Does this mean programming language nerds get to play too, maybe after boning up on our calculus and topology?
Re: “Deep Learning has outlived its usefulness as a buzz-phrase”
#55I wish we could come up with a catchier name, but I LOVE the idea of calling this programming , because that is precisely what we do when we compose deep neural nets. For example, here's how you compose a neural net consisting of two "dense" layers (linear transformations), using Keras's functional API, and then apply these two layers to some tensor x to obtain a tensor y: f = Dense(n) g = Dense(n) y = f(g(x)) This l…
I hate the name, but LOVE the idea of calling this programming... What would you call it instead?
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Re: “Deep Learning has outlived its usefulness as a buzz-phrase”
#56I wish we could come up with a catchier name, but I LOVE the idea of calling this programming , because that is precisely what we do when we compose deep neural nets. For example, here's how you compose a neural net consisting of two "dense" layers (linear transformations), using Keras's functional API, and then apply these two layers to some tensor x to obtain a tensor y: f = Dense(n) g = Dense(n) y = f(g(x)) This l…
I hate the name, but LOVE the idea of calling this programming... What would you call it instead?
I changed "hate the name" to "wish we could come up with a catchier name," which better reflects what I meant to write.
Re: “Deep Learning has outlived its usefulness as a buzz-phrase”
#57Does this mean programming language nerds get to play too, maybe after boning up on our calculus and topology?
They already are; e.g. Jeff Dean among many others. The question is will the PL academic community play as well.
Conal Elliott has done a lot of work in this area about 10 years ago. His work is beautiful but maybe before it’s time.
Re: “Deep Learning has outlived its usefulness as a buzz-phrase”
#58I wish we could come up with a catchier name, but I LOVE the idea of calling this programming , because that is precisely what we do when we compose deep neural nets. For example, here's how you compose a neural net consisting of two "dense" layers (linear transformations), using Keras's functional API, and then apply these two layers to some tensor x to obtain a tensor y: f = Dense(n) g = Dense(n) y = f(g(x)) This l…
LeCun specifically calls out imperative programming, not just typical data flow methods.
Re: “Deep Learning has outlived its usefulness as a buzz-phrase”
#59Earlier quoted context omitted.
So it's still just neural nets? Cool -- we've seen that before.
Is a single SGD layer a neural net? Is an image filter or an audio filter a neural net? Is matrix multiplication a neural net? This would strain the intended definition even farther than it's already been strained. But all of those are differentiable programming, and rightly so because they're all pieces that you use and compose together to make interesting learning mechanisms, including the ones that we vaguely refe…
I'm not really buying your argument here. Neural networks are just a collection of artificial neurons. There is no requirement for multiple layers or depth of any kind.
Re: “Deep Learning has outlived its usefulness as a buzz-phrase”
#60"working on compilers for imperative differentiable programming languages" what would be an example of such language?
BrainScript was one, though it seems deprecated ATM:
https://docs.microsoft.com/en-us/cognitive-toolkit/brainscri...
Compilers might not be involved, interpreters can work just as well if most of the heavy lifting is in the solving.