5 ms·

"Deep" is such a good prefix for all sorts of Deep Learning, Deep Math, Deep Thinking, Deep Engineering etc. Wonder if the networks were originally called Thick

"Deep" is such a good prefix for all sorts of Deep Learning, Deep Math, Deep Thinking, Deep Engineering etc. Wonder if the networks were originally called Thick neural networks, would the ML/AI revolution as we know it still happened?

WanderPanda 6y ago
If thick neural networks would indeed exist they would probably be a special case of something Schmidhuber invented 30 years ago.
- webmaven 6y ago
  > If thick neural networks would indeed exist they would probably be a special case of something Schmidhuber invented 30 years ago. Heh. A burn both pointed and subtle. A+.
subtypefiddler 6y ago
Whilst I agree with the general sentiment, in this particular instance it has to do with the depth of network that could be trained efficiently thanks to hardware advances. LeNet was 7 layers deep, Dan's 9, VGG's 13, GoogleNet's 22, etc. There is theory w.r.t to thick networks as well (e.g the link to Gaussian processes require infinite width). Deep makes sense here.
- The_rationalist 6y ago
  Well except that most neural network are not deep, they have a very low number of layers but each layer can be tremendously wide. This should have been called wide learning. But we could imagine some learning algorithm that exploit more depth than wideness. A more correct naming would take into account both dimensions: depth and wideness. Note that this is hortogonal to sparsedness vs density
  - subtypefiddler 6y ago
    It's also important to note that they work despite being wide, you can see that with the efficiency of pruning, and ideas such as the lottery ticket hypothesis that state that "successful" sub-networks within the wide network account for most of the performance. In the theory literature, if you have a K-deep network, K=1 is the shallow case, K>1 is deep. Agreed naming could be better, but it's not like "deep work" or "deep thoughts" as the parent was stating.
  - sdenton4 6y ago
    I dunno, in the resnet age, many and perhaps most networks are 20+ layers. I feel like the shallowest networks I see these days are RNNs being used for fast on device ML, which trends not to be terribly wide due to the same hardware constraints.
  - canjobear 6y ago
    Around the time the phrase "deep learning" came into vogue, the advances were indeed in training deeper networks, not wider. Later on it turned out that shallow wide networks are sufficient for many problems. (Also, it turned out the pre-training tricks that people came up with for training deep networks weren't really necessary either.)
  - laleopue 6y ago
    The adjective "deep" came from deep belief networks, which are a variation on restricted boltzmann machines. RBNs have one visible and one hidden layers, DNBs have more hidden layers - hence "deep". So it's not exactly based on a distinction between "deep" and "shallow" models.
  - antognini 6y ago
    The depth seems to matter more than the width, at least as long as the layers are sufficiently wide. In fact, in the limit that the layer becomes infinitely wide, you just end up with a Gaussian process. In practice a width of ~100--1000 is sufficient to get behavior that is pretty close to a Gaussian process, so in general doubling the width of a layer doesn't gain you all that much compared to using those parameters for an extra layer. The real representational power seems to come from increasing depth.
thecleaner 6y ago
> ....Thick neural networks, would the ML/AI revolution as we know it still happened? Cancel culture will ensure that the conference doesnt happen in the name of fat shaming.
- cool_dude85 6y ago
  Finger on the pulse, my man - people HATE being called thick these days.
  - deleted 6y ago
    [deleted]
naringas 6y ago
it's the the new e-
TrackerFF 6y ago
Of all the nouns available (deep, dense, thick, big, condensed, etc.), "deep" is def. the on that brings most hype - makes it sound very advanced, and more marketable. Deep Learning with Big Data That stuff sells itself.
- AnHonestComment 6y ago
  Bruh, it’s all about “multi-mode learning with hyper-scale data”, you’re like five years late. The lingo treadmill is brutal.

WanderPanda 6y ago

If thick neural networks would indeed exist they would probably be a special case of something Schmidhuber invented 30 years ago.

webmaven 6y ago
> If thick neural networks would indeed exist they would probably be a special case of something Schmidhuber invented 30 years ago. Heh. A burn both pointed and subtle. A+.

subtypefiddler 6y ago

Whilst I agree with the general sentiment, in this particular instance it has to do with the depth of network that could be trained efficiently thanks to hardware advances. LeNet was 7 layers deep, Dan's 9, VGG's 13, GoogleNet's 22, etc. There is theory w.r.t to thick networks as well (e.g the link to Gaussian processes require infinite width). Deep makes sense here.

The_rationalist 6y ago
Well except that most neural network are not deep, they have a very low number of layers but each layer can be tremendously wide. This should have been called wide learning. But we could imagine some learning algorithm that exploit more depth than wideness. A more correct naming would take into account both dimensions: depth and wideness. Note that this is hortogonal to sparsedness vs density
- subtypefiddler 6y ago
  It's also important to note that they work despite being wide, you can see that with the efficiency of pruning, and ideas such as the lottery ticket hypothesis that state that "successful" sub-networks within the wide network account for most of the performance. In the theory literature, if you have a K-deep network, K=1 is the shallow case, K>1 is deep. Agreed naming could be better, but it's not like "deep work" or "deep thoughts" as the parent was stating.
- sdenton4 6y ago
  I dunno, in the resnet age, many and perhaps most networks are 20+ layers. I feel like the shallowest networks I see these days are RNNs being used for fast on device ML, which trends not to be terribly wide due to the same hardware constraints.
- canjobear 6y ago
  Around the time the phrase "deep learning" came into vogue, the advances were indeed in training deeper networks, not wider. Later on it turned out that shallow wide networks are sufficient for many problems. (Also, it turned out the pre-training tricks that people came up with for training deep networks weren't really necessary either.)
- laleopue 6y ago
  The adjective "deep" came from deep belief networks, which are a variation on restricted boltzmann machines. RBNs have one visible and one hidden layers, DNBs have more hidden layers - hence "deep". So it's not exactly based on a distinction between "deep" and "shallow" models.
- antognini 6y ago
  The depth seems to matter more than the width, at least as long as the layers are sufficiently wide. In fact, in the limit that the layer becomes infinitely wide, you just end up with a Gaussian process. In practice a width of ~100--1000 is sufficient to get behavior that is pretty close to a Gaussian process, so in general doubling the width of a layer doesn't gain you all that much compared to using those parameters for an extra layer. The real representational power seems to come from increasing depth.

thecleaner 6y ago

> ....Thick neural networks, would the ML/AI revolution as we know it still happened? Cancel culture will ensure that the conference doesnt happen in the name of fat shaming.

cool_dude85 6y ago
Finger on the pulse, my man - people HATE being called thick these days.
- deleted 6y ago
  [deleted]

naringas 6y ago

it's the the new e-

TrackerFF 6y ago

Of all the nouns available (deep, dense, thick, big, condensed, etc.), "deep" is def. the on that brings most hype - makes it sound very advanced, and more marketable. Deep Learning with Big Data That stuff sells itself.

AnHonestComment 6y ago
Bruh, it’s all about “multi-mode learning with hyper-scale data”, you’re like five years late. The lingo treadmill is brutal.