Viewing profile — SoerenL

Yeah, I am referring to Ricci notation. TF and PyTorch don't use it. The new version already has relus. This version is targeted at standard formulas (has abs as a non-differentiab…

True, for large-scale problems Hessian-vector products are often the way to go (or completely ignoring second order information). However, computing first an expression for the Hes…

Not in its current formulation. It uses a different representation of the tensors. However, a new version/algorithm that will be available in a few months can be used in TF and PyT…

That's what is usually done, autodiff on the component wise expression. We don't do it here. Instead, we really compute on the matrix and tensor level and compute derivatives here …

Do you need Hessians or Jacobians for computing the earth mover distance, then a definite yes. Otherwise, I would doubt it (though I do not know exactly.)

As one of the authors: Yes, it is true, 3 orders of magnitude (so about a factor of 1000 on GPUs). But please be careful, this holds only for higher order derivatives (like Hessian…

But how do you compute the derivative of x' A x in Mathematica (x being a vector and A being a matrix)? What you have pointed out is only scalar derivatives, if I am not mistaken h…

XLA is good for the GPU only. On the CPU MC is about 20-50% faster than TF on scalar valued functions. For the GPU I don't know yet. But it is true that for augmented Lagrangian yo…

I did not compare to Tensorflow XLA but I compared it to Tensorflow. Of course, it depends on the problem. For instance, for evaluating the Hessian of x' A x MC is a factor of 100 …

As one of the authors of this tool I understand that you would like to have some way of trusting the output. Internally we check it numerically, i.e., generate some random data for…

It is not in the current online tool but we will add it again soon. It is still in there the way you describe it (passing transpose down to the leaves and simplification rules as w…