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ADEV: Sound Automatic Differentiation of Expected Values of Probabilistic Programs

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Optimization of Stochastic Models

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Correctness of Automatic Differentiation via Diffeologies and Categorical Gluing

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Densities of Almost Surely Terminating Probabilistic Programs are Differentiable Almost Everywhere

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Implicit Reparameterization Gradients

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Reparameterization Gradient for Non-differentiable Models

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Pathwise Derivatives Beyond the Reparameterization Trick

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Pyro: Deep Universal Probabilistic Programming

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Monte Carlo Gradient Estimation in Machine Learning

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Denotational Correctness of Forward-Mode Automatic Differentiation for Iteration and Recursion

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Automatic Differentiation of Programs with Discrete Randomness

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Pelatihan “The Fundamental of Smart Programming” PKBM Bina Karya Semarang

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