Giles' blog

Dropout and mandatory vacation

As I was dozing off the other night, after my post on dropout, it popped into my mind that it's not dissimilar to something many financial firms do. They require certain key employees to take at least two consecutive weeks of holiday every year -- not because they're kind employers who believe in a healthy work-life balance (source: I worked for one) but because it makes sure the firm is functioning safely and effectively, at a small cost in performance.

There are two reasons this helps:

1. It reduces key-person risk. By enforcing vacation like this, they make absolutely sure that the business can continue to operate even if some people are out. If stuff goes wrong while they're out, then obviously processes are broken or other people don't have the knowledge they need to pick up the slack. So long as it's well-managed, those problems can be fixed, which means that if the key people quit, there's less damage done. Think of it as being like reducing the bus number of a dev team.
2. It can uncover misbehaviour. Let's imagine a trader is doing something they shouldn't -- maybe fraud, or perhaps just covering up for their mistakes so that they don't get fired. They might be able to manage that by shuffling balances around if they're in the office every day, but two weeks out should mean that whoever is covering for them will work out that something isn't right.

Now, I should admit that the second of those (a) doesn't really apply to dropout ¹ and (b) is probably the more important of the two from a bank's perspective.

But the first, I think, is a great metaphor for dropout during training. What we want to do is make sure that no particular parameter is "key"; we want the knowledge and intelligence to be spread across the model as a whole.

That also clears up a couple of questions I had about dropout:

• It slows down training. Yes, if you're doing dropout, you'll see your error falling more slowly than if you don't -- just like the trading desk sees their performance drop a bit when their top trader is on mandatory vacation. But that's a cost you pay to gain performance at other times -- the rest of the year for the bank, or at inference time for the model.
• Do you keep gradients for, and back-prop to, the dropped-out parameters? No, just like the bank wouldn't put the people who were out of the office through training for issues that came up during their absence. They'd train the people or fix the systems that had problems instead.

Now, is this a perfect metaphor, or even a great one? Maybe not. But it works for me, and I thought I'd share it in case it's useful for anyone else. And I'm going to be looking for similar organisational metaphors for other ML techniques -- I think they are a useful way to clarify things, especially for those of us who (for better or for worse) have spent time in the trenches of actual organisations.