3 ms·

The idea might be cute but performance is not there yet. Specifically they were able to achieve state of the art performance on MNIST, but got 10.6% test error

The idea might be cute but performance is not there yet. Specifically they were able to achieve state of the art performance on MNIST, but got 10.6% test error on CIFAR 10 which is comparable to state of the art of 4 years ago (and if you're in the field, 4 years is like a century ago). It's important to stress that there's ABSOLUTELY NO theory backing anything so everything we're doing including this idea of capsules and dynamic routing is just brute-forcing, trial-and-error. Even though the idea is cute, there's still ALOT to be proven for this method. So when I see all these articles, I feel a little bit uneasy.

topynate 9y ago
Okay, but it sounds like this technique is targeting learning from a limited number of examples. How does it compare on CIFAR 10 when restricted to ten training images per class? That's the sort of thing that you don't even see in benchmarks, because there hasn't been any way to get a handle on the problem.
- inverse_pi 9y ago
  That is not true. What you're describing is called zero to a few shot learning in the literature and there are specific benchmarks to test this. The author(s) specifically chose to NOT test on these benchmarks and there is no explicit mentions in the paper that they wanted to target a few shot learning.
zardo 9y ago
>just brute-forcing, trial-and-error. I think as a whole, the community is executing a distributed epsilon-greedy montecarlo search, which is theoretically guaranteed to converge on an optimal policy eventually.
- goptimize 9y ago
  when time goes to infinity which is a shitty guarantee
- inverse_pi 9y ago
  not necessarily. The convergence point can be and will probably be sub-optimal if we keep doing this without a mathematical framework guiding us.
  - zardo 9y ago
    So long as you never stop trying random things, you are guaranteed to find a global optimum... as time->inf Not a very useful guarantee, but that's theoretical guarantees for you.
modzu 9y ago
actually the theory comes from the brain https://en.wikipedia.org/wiki/Cortical_minicolumn https://en.wikipedia.org/wiki/Cortical_minicolumn
- inverse_pi 9y ago
  that's just an aspiration, not mathematical foundation.

topynate 9y ago

Okay, but it sounds like this technique is targeting learning from a limited number of examples. How does it compare on CIFAR 10 when restricted to ten training images per class? That's the sort of thing that you don't even see in benchmarks, because there hasn't been any way to get a handle on the problem.

inverse_pi 9y ago
That is not true. What you're describing is called zero to a few shot learning in the literature and there are specific benchmarks to test this. The author(s) specifically chose to NOT test on these benchmarks and there is no explicit mentions in the paper that they wanted to target a few shot learning.

zardo 9y ago

>just brute-forcing, trial-and-error. I think as a whole, the community is executing a distributed epsilon-greedy montecarlo search, which is theoretically guaranteed to converge on an optimal policy eventually.

goptimize 9y ago
when time goes to infinity which is a shitty guarantee
inverse_pi 9y ago
not necessarily. The convergence point can be and will probably be sub-optimal if we keep doing this without a mathematical framework guiding us.
- zardo 9y ago
  So long as you never stop trying random things, you are guaranteed to find a global optimum... as time->inf Not a very useful guarantee, but that's theoretical guarantees for you.

modzu 9y ago

actually the theory comes from the brain https://en.wikipedia.org/wiki/Cortical_minicolumn https://en.wikipedia.org/wiki/Cortical_minicolumn

inverse_pi 9y ago
that's just an aspiration, not mathematical foundation.