8 citations · 8 across the 1 of their papers we have counts for
2 papers
cs.LG2026
Coordinate-Free Automatic Differentiation
Alex Guerra, Joshua Aduol, Yaqian Tang +3
Automatic differentiation (AD) is a cornerstone of modern machine learning. Since all AD frameworks implicitly fix a basis of Euclidean space to define gradients and Jacobians, the…
cs.LG2020★ 8 cited
Randomized Automatic Differentiation
Deniz Oktay, Nick McGreivy, Joshua Aduol +2
The successes of deep learning, variational inference, and many other fields have been aided by specialized implementations of reverse-mode automatic differentiation (AD) to comput…