Learning to learn by gradient descent by gradient descent | Hacker News Reader

Learning to learn by gradient descent by gradient descent

arxiv.org

story time: I tried something like this, putting a nnet output as the transfer weight of another neural net and training the first one feeding in the second net input as input and training it on the second net error, but couldn't train the first network because I didn't know how to derive the transfer function for the back propagation algorithm.

so I opted for training the first net using a randomized genetic algorithm and function descent on it, which as an afterthought is dangerously close on how biology kind of work, but it was exceptionally slow.

so I split up the training batches, went to the uni computer room and left the job running on every computer by night to collect result by morning. in the morning I'd collect the best genes from each machine, mix them all for another few round of training, select the best in the population and reseed them on all the machines by night.

after a week of painstakingly organizing, seeding and collecting results, the network never managed to converge around the problem, but boy it was fun trying! The problem was driving a car around a lap of a track using five "distance from kerb" sensor as input angled at 30deg from each other starting from center.

I remember I was inspired by an image recognition company, which was using a training network for training network for motion detection over security cameras, so this approach wasn't exactly novelty even back then (2001ish).

anyway, this got me noticed by a lab assistant and got a thesis on how to optimize neural network to run in 4.4bit fixed math for use in extra low power devices. that one worked! too bad nothing ever came out of it.

edit: some fixin

That fact they nothing came out of it is the result of your research ! keep doing the good work :)

See dp4a on GTX1080. You're a hot commodity if you can build on that. See also Google's TPU where they managed 8-bit inference but probably not 8-bit training (though that may be more of a memory or bandwidth limitation).

but reduced precision neural network evaluation is a really hot field right now!

eh, I have the paper and the code on hand if anyone's interested, but it's printed because thesis. also matlab but that's minor.

Yes, please. I'd be happy with some photos snapped from your phone if you don't want to scan it in. The Matlab code in particular seems intriguing.

this was ten years ago, so techniques are severely outdated etc. also it's not THAT much of code, the majority of it is standard initialization and optimization. here's the most important stuff

https://imgur.com/a/w2BCy

Just read (skimmed) this paper yesterday actually.

Looks interesting-- but there are no timing graphs! It's kind of a strawman argument to say "We can't use Newton's method because it's too slow to calculate the Hessian," and then go and present all your performance graphs in terms of number of iterations.

Usually not providing time measurements means that each iteration is extravagantly expensive and the authors didn't find test cases with good actual performance, but in this case there seems to be the major twist of completely hiding away the optimizer training cost.

To be fair, it should be noted that there are no claims of actual good performance, only claims that the technology works: "Our experiments have confirmed that learned neural optimizers compare favorably against state-of-the-art optimization methods used in deep learning."

Neither the paper nor the comments have mentioned this: https://en.wikipedia.org/wiki/Truncated_Newton_method

The Truncated Newton method uses an inner solver that only runs for a few iterations to approximate the Hessian. The approximate Hessian is used to approximately solve Newtons equation. I've implemented it and it works very well. When it gets close to the solution the convergence is very fast.

I mention it because it sounds similar to what the paper discusses but you use conjugate gradients in the inner solver and Newton's equation in the outer solver.

The title could have been chosen as "Learn to learn by gradient descent by gradient descent" or "Learning learning by gradient descent by gradient descent"

Or "Learn learning by gradient descent by gradient descent." All you're doing is changing the verb tenses... All you've done is change the verb tenses... All you did is change the verb tenses

This comment is perfect!

edit: guys it's a pun

This comment will have already been perfect.

The title is a play on the 2001 paper:

"Learning To Learn Using Gradient Descent" by Hochreiter et al.

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.5.32...

Can all algorithms then be cast as learning problems and their optimal versions produced this way? Seems like amazing work, but I don't know enough to confirm.

Gradient descent has local maxima problems, so it's not always going to produce an "optimal" result.

Humans also have local maxima problems

Just this one last cigarette - in the pursuit of happinnes

Gradient ascent reaches a local maximum eventually, but gradient descent is guaranteed to find local minima only.

They're the same thing, give or take a minus sign... right?

Indeed

  max {f(x): x in X} = - min {-f(x): x in X}

However, gradient ascent on a convex minimization problem will get stuck in a local maximum (as a convex minimization problem has f(x) convex, hence with local minima = global minima), and viceversa for gradient descent algorithms on concave maximization problems.

There are a ton of auto tuning PID algorithms out there, which is somewhat analogous. Unfortunately they only work for certain regions. No free lunch (theorem)!

The resolution of the Entscheidungsproblem would seem to answer this question in the negative:

http://en.wikipedia.org/wiki/Entscheidungsproblem

However, it does not place limits on the quality of approximation. This suggests that the development of AI will proceed gradually rather than suddenly (I've always disliked the term "singularity"; exponentials don't have those) since the infinite case is not going to be solved.

The name is "automatic programming", or at least it was by the times the books available at the library in my undergrad were written (what was much earlier than my undergrad).

Genetic programming in special is very interesting, and worth reading about.

Very interesting, I am eager to read about the future research on non "simple convex problems". This where it could provide benefits as in the industry we have a lot of them, a lot of domain knowledge to go around the local minima etc. and a robust ML based approach could really help there instead of being obliged to accumulate in our algorithms years of trial and errors.

Yo dawg, I heard you like gradient descent, so I put optimizers on your optimizer so you can learn while you learn.

No timing results, no comparisons with Nesterov type methods. To all the commenters that have said "this looks promising": this doesn't look promising at all. Why do you think in all the years of people optimizing things with gradient descent no one has tried this? Answer: they have, and it doesn't work.

Timing results are a poor substitute to O(N) complexity estimates.

I mean, does a method take longer because it's doing lots of virtual memory stuff or because it uses a lot of computron?

In spite of this, optimization algorithms are still designed by hand.

Well, they are tuned automatically. There are derivative-free optimization algorithms that have been designed to tune optimization algorithms on a set of instances.

I was just thinking about this the other day! If machine learning can be applied to almost any problem, then surely it could be applied recursively to optimize itself. I'm glad to see that someone worked on this.

The first time I read about gradient descent and optimization algorithms, this was the first thing that came to my mind, this looks promising.

I actually do hope that all those learning techniques do have limitations and that intelligence cannot be achieved in principle by machine learning. OK, I see the problems with that statement (first of all, define intelligence) but for instance, I hope the so-called "singularity" cannot be reached in principle and if somebody could prove it once and for all, please do so.

There's an interesting question about the return on investment for self-improvement. The singularity idea is that after a self-modifying algorithm makes an improvement to itself, it becomes better at improving itself, and hence the next improvement is found more quickly.

There's also another aspect though: the first improvement will (by definition) be the easiest to find, and subsequent improvements might get harder and harder to find. This acts to slow down self-improvement.

I think it's interesting to consider which of these will dominate in a particular domain, and my own research is related.

That would indeed be comfortable.

When I just finished Andrew Ng ML course and had to solve a real life problem, this was actually the first thing that came into my mind. Too bad I couldn't formulate the problem then(still can't) and that was basically the end of my ML career.

Next article: "Learning to learn to learn by gradient descent by gradient descent by gradient descent".

Then "Learning to learn to learn to learn by gradient descent by gradient descent by gradient descent by gradient descent" and keep going. Turtles all the way down!

P.s: I understand the beauty of this article, but I was surprised none get this irony :-)

I think everyone gets it -- the article is intentionally given the slightly cheeky title.

Yo dawg, I heard you liked gradients, so I put some learning in your learning so you can descend while you descend.

http://knowyourmeme.com/memes/xzibit-yo-dawg

You beat me to it! I made one.

http://imgur.com/HI8d0B1

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