[ Parent Question — Does the lottery ticket hypothesis suggest the scaling hypothesis? ]

What happens to variance as neural network training is scaled? What does it imply about "lottery tickets"?

by Abram Demski1 min read28th Jul 20201 comment


AI TimelinesAI

Daniel Kokotajlo asks whether the lottery ticket hypothesis implies the scaling hypothesis.

The way I see it, this depends on the distribution of "lottery tickets" being drawn from.

  • If the quality of lottery tickets follows a normal distribution, then after your neural network is large enough to sample decent tickets, it will get better rather slowly as you scale it -- you have to sample a whole lot of tickets to get a really good one.
  • If the quality of tickets has a long upward tail, then you'll see better scaling.

However, a long tail also suggests to me that variance in results would continue to be relatively high as a network is scaled: bigger networks are hitting bigger jackpots, but since even bigger jackpots are within reach, the payoff of scaling remains chaotic.

(This could all benefit from a more mathematical treatment.)

So: what do we know about NN training? Does it suggest we are living in extremistan or mediocristan?

Note: a major conceptual difficulty to answering this question is representing NN quality in the right units. For example, an accuracy metric -- which necessarily falls between 0% and 100% -- must yield "diminishing returns", and cannot be host to a "long-tailed distribution". Take that same metric and send it through an inverse sigmoid, and now you might not have diminishing returns, and could have a long-tail distribution. But we can transform data all day. The analysis shouldn't be too ad-hoc. So it's not immediately clear how to measure this.

New Answer
Ask Related Question
New Comment
1 comments, sorted by Highlighting new comments since Today at 12:17 PM

One related question is what sub-tasks of gpt-3 showed surprise jackpots vs gpt-2