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Useful Algorithms That Are Not Optimized by Jax, PyTorch, or TensorFlow
There is no free lunch:).
I remember spending a summer using Template Model Builder (TMB), which is a useful R/C++ automatic differentiation (AD) framework, for working with accelerated failure time models. For these models, the survival to time T given covariates X is defined by S(tX) = P(T>tX) = S_0(t exp(beta^T X)) for baseline survival S_0(t). I wanted to use splines for the baseline survival and then use AD for gradients and random effects. Unfortunately, after implementing the splines in template C++, I found a web page entitled "Things you should NOT do in TMB" (https://github.com/kaskr/adcomp/wiki/ThingsyoushouldNOTd...)  which included using if statements that are based on coefficients. In this case, the splines for S_0 depend on beta, which is this specific excluded case:(. An older framework (ADMB) did not have this constraint, but dissemination of code was more difficult. Finally, PyTorch did not have an implementation of Bsplines or an implementation for Laplace's approximation. Returning to my opening comment, there is no free lunch.
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kaskr/adcomp is an open source project licensed under GNU General Public License v3.0 or later which is an OSI approved license.