I 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.
“Deep Learning has outlived its usefulness as a buzz-phrase”
61–70 of 107 posts
Re: “Deep Learning has outlived its usefulness as a buzz-phrase”
#62Off topic. Yann LeCun really looks like Michael Moore who looks like Peter Griffin.
Re: “Deep Learning has outlived its usefulness as a buzz-phrase”
#63[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.
Re: “Deep Learning has outlived its usefulness as a buzz-phrase”
#64[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.
Re: “Deep Learning has outlived its usefulness as a buzz-phrase”
#65[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.
Ordinary Metropolis-Hastings, for example, is closer to trial and error.
Re: “Deep Learning has outlived its usefulness as a buzz-phrase”
#66Earlier quoted context omitted.
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”
#67"See more of Yann LeCun on Facebook" popup, no access to the page. No, I don't want to create a Facebook account to read a blog post. Perhaps links to walled-garden pages where you need an account and need to be logged in should be prohibited or at least discouraged.
Re: “Deep Learning has outlived its usefulness as a buzz-phrase”
#68"See more of Yann LeCun on Facebook" popup, no access to the page. No, I don't want to create a Facebook account to read a blog post. Perhaps links to walled-garden pages where you need an account and need to be logged in should be prohibited or at least discouraged.
this will get downvoted as all held, but I really think most socially apt people should have Facebook accounts these days
Re: “Deep Learning has outlived its usefulness as a buzz-phrase”
#69Earlier quoted context omitted.
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.
As much of a pity that, 70 years later, we are still using transistor based computers originally derived from three wires stuck in a piece of rock[1] by some very innovative fellows at Bell Labs[2]? [1] http://images.computerhistory.org/revonline/images/500004836... [2] http://www.computerhistory.org/revolution/digital-logic/12/2...
Re: “Deep Learning has outlived its usefulness as a buzz-phrase”
#70Earlier quoted context omitted.
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.
Following a gradient is smarter than trial and error. You can make an argument that, in high-dimensional parameter spaces, it’s hard to do better (because, gradient descent is linear in the number of dimensions). Ordinary Metropolis-Hastings, for example, is closer to trial and error.
Yes, but increasing dimensions does not mean the manifold of the problem space increases the same.