Or has at least found that existing solutions for statically typed "differentiable" programming are ineffective, and I'd agree.
But having some way to check types/properties of tensors that you are doing operations to would really help to make sure you don't get your one hidden dimension accidentally switched with the other or something. Some of these problems are silent and need something other than dynamic runtime checking to find them, even if it's just a bolt-on type checker to python.
There are a lot of issues with our current approach of just using memory and indexed dimensions. [0]
Or has at least found that existing solutions for statically typed "differentiable" programming are ineffective, and I'd agree.
But having some way to check types/properties of tensors that you are doing operations to would really help to make sure you don't get your one hidden dimension accidentally switched with the other or something. Some of these problems are silent and need something other than dynamic runtime checking to find them, even if it's just a bolt-on type checker to python.
There are a lot of issues with our current approach of just using memory and indexed dimensions. [0]
[0]: http://nlp.seas.harvard.edu/NamedTensor