Differentiating Metropolis-Hastings to Optimize Intractable Densities

1 code implementation • 13 Jun 2023 • Gaurav Arya, Ruben Seyer, Frank Schäfer, Kartik Chandra, Alexander K. Lew, Mathieu Huot, Vikash K. Mansinghka, Jonathan Ragan-Kelley, Christopher Rackauckas, Moritz Schauer

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.

Automatic Differentiation of Programs with Discrete Randomness

1 code implementation • 16 Oct 2022 • Gaurav Arya, Moritz Schauer, Frank Schäfer, Chris Rackauckas

Automatic differentiation (AD), a technique for constructing new programs which compute the derivative of an original program, has become ubiquitous throughout scientific computing and deep learning due to the improved performance afforded by gradient-based optimization.

End-to-End Optimization of Metasurfaces for Imaging with Compressed Sensing

no code implementations • 28 Jan 2022 • Gaurav Arya, William F. Li, Charles Roques-Carmes, Marin Soljačić, Steven G. Johnson, Zin Lin

We present a framework for the end-to-end optimization of metasurface imaging systems that reconstruct targets using compressed sensing, a technique for solving underdetermined imaging problems when the target object exhibits sparsity (i. e. the object can be described by a small number of non-zero values, but the positions of these values are unknown).

Object Super-Resolution