jhomedall · HackerTrans

I have a VR headset and still prefer head tracking. It's more comfortable, and becomes second nature after an hour or two of use.

jhomedall

3 years agodiscuss

There is no way to determine that a non-trivial neural network won't drastically diverge in output due to small changes in input (eg one pixel attacks on image classifiers). This is true for all current models I know of.

Almost all neural network implementations have continuous outputs (ie the nodes in the output layer produce a value between 0 and 1). That doesn't change the above issue at all.

This is much less of an issue with traditional methods

jhomedall

3 years agodiscuss

That's useless in this case. You need to be able to prove that it will work with all inputs, and there are too many combinations of inputs to exhaustively enumerate.

jhomedall

4 years agodiscuss

I've worked on several teams that required PRs before merging, and there was never any toxic behavior. Everybody was happy with it.

jhomedall

5 years agodiscuss

F#, Kotlin, Python, Nim and many others all seem to get by fine without semicolons as statement terminators.

jhomedall

8 years agodiscuss

You would implement the sort so that it either:

1: Returns the sorted list (once finished)

2: Returns an intermediate state containing the progress of the sort in addition to the current state of the sort.

You would repeatedly pass the intermediate state to the sorting function until finished. You can use the progress component of the intermediate state to track progress.

Something like the following (I don't actually write Haskell, so this could probably be represented better):

  data SomeIntermediateState = ...
  data Progress = Double
  
  data SortState a =
    Complete [a]  
    | InProgress (SomeIntermediateState, Progress)
  
  sortInit :: [a] -> SortState
  sort :: SortState -> SortState

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