17 citations · 59 across the 17 of their papers we have counts for
4 papers · 1 filter
Efficient and Sound Differentiable Programming in a Functional Array-Processing Language
Amir Shaikhha, Mathieu Huot, Shabnam Ghasemirad +3
Automatic differentiation (AD) is a technique for computing the derivative of a function represented by a program. This technique is considered as the de-facto standard for computi…
ADEV: Sound Automatic Differentiation of Expected Values of Probabilistic Programs
Alexander K. Lew, Mathieu Huot, Sam Staton +1
Optimizing the expected values of probabilistic processes is a central problem in computer science and its applications, arising in fields ranging from artificial intelligence to o…
Denotationally Correct, Purely Functional, Efficient Reverse-mode Automatic Differentiation
Mathieu Huot, Amir Shaikhha
Reverse-mode differentiation is used for optimization, but it introduces references, which break the purity of the underlying programs, making them notoriously harder to optimize.…
Compiling Structured Tensor Algebra
Mahdi Ghorbani, Mathieu Huot, Shideh Hashemian +1
Tensor algebra is essential for data-intensive workloads in various computational domains. Computational scientists face a trade-off between the specialization degree provided by d…