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The elements of differentiable programming
Artificial intelligence has recently experienced remarkable advances, fueled by large
models, vast datasets, accelerated hardware, and, last but not least, the transformative …
models, vast datasets, accelerated hardware, and, last but not least, the transformative …
A-nesi: A scalable approximate method for probabilistic neurosymbolic inference
We study the problem of combining neural networks with symbolic reasoning. Recently
introduced frameworks for Probabilistic Neurosymbolic Learning (PNL), such as …
introduced frameworks for Probabilistic Neurosymbolic Learning (PNL), such as …
Automatic differentiation of programs with discrete randomness
Automatic differentiation (AD), a technique for constructing new programs which compute
the derivative of an original program, has become ubiquitous throughout scientific …
the derivative of an original program, has become ubiquitous throughout scientific …
ADEV: Sound automatic differentiation of expected values of probabilistic programs
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 operations …
science and its applications, arising in fields ranging from artificial intelligence to operations …
Sparse graph learning from spatiotemporal time series
Outstanding achievements of graph neural networks for spatiotemporal time series analysis
show that relational constraints introduce an effective inductive bias into neural forecasting …
show that relational constraints introduce an effective inductive bias into neural forecasting …
Auto-differentiation of relational computations for very large scale machine learning
The relational data model was designed to facilitate large-scale data management and
analytics. We consider the problem of how to differentiate computations expressed …
analytics. We consider the problem of how to differentiate computations expressed …
Branches of a tree: Taking derivatives of programs with discrete and branching randomness in high energy physics
We propose to apply several gradient estimation techniques to enable the differentiation of
programs with discrete randomness in High Energy Physics. Such programs are common in …
programs with discrete randomness in High Energy Physics. Such programs are common in …
Probabilistic programming with programmable variational inference
Compared to the wide array of advanced Monte Carlo methods supported by modern
probabilistic programming languages (PPLs), PPL support for variational inference (VI) is …
probabilistic programming languages (PPLs), PPL support for variational inference (VI) is …
Discovering dynamics and parameters of nonlinear oscillatory and chaotic systems from partial observations
Despite rapid progress in data acquisition techniques, many complex physical, chemical,
and biological systems remain only partially observable, thus posing the challenge to …
and biological systems remain only partially observable, thus posing the challenge to …
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 …
allowing us to differentiate through probabilistic inference, even if the model has discrete …