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Found 5 papers, 1 papers with code
Differentiating Metropolis-Hastings to Optimize Intractable Densities
We develop an algorithm for automatic differentiation of Metropolis-Hastings samplers, allowing us to differentiate through probabilistic inference, even if the model has discrete components within it.
$\nabla$SD: Differentiable Programming for Sparse Tensors
Sparse tensors are prevalent in many data-intensive applications, yet existing differentiable programming frameworks are tailored towards dense tensors.
$ω$PAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs
We introduce a new setting, the category of $\omega$PAP spaces, for reasoning denotationally about expressive differentiable and probabilistic programming languages.
Efficient and Sound Differentiable Programming in a Functional Array-Processing Language
Automatic differentiation (AD) is a technique for computing the derivative of a function represented by a program.
Functional Collection Programming with Semi-Ring Dictionaries
We developed SDQL, a statically typed language that can express relational algebra with aggregations, linear algebra, and functional collections over data such as relations and matrices using semi-ring dictionaries.
