Addressing Discontinuous Root-Finding for Subsequent Differentiability in Machine Learning, Inverse Problems, and Control

no code implementations • 21 Jun 2023 • Daniel Johnson, Ronald Fedkiw

There are many physical processes that have inherent discontinuities in their mathematical formulations.

Software-based Automatic Differentiation is Flawed

no code implementations • 5 May 2023 • Daniel Johnson, Trevor Maxfield, Yongxu Jin, Ronald Fedkiw

Various software efforts embrace the idea that object oriented programming enables a convenient implementation of the chain rule, facilitating so-called automatic differentiation via backpropagation.

A Library for Representing Python Programs as Graphs for Machine Learning

1 code implementation • 15 Aug 2022 • David Bieber, Kensen Shi, Petros Maniatis, Charles Sutton, Vincent Hellendoorn, Daniel Johnson, Daniel Tarlow

Graph representations of programs are commonly a central element of machine learning for code research.

Latent Gaussian Activity Propagation: Using Smoothness and Structure to Separate and Localize Sounds in Large Noisy Environments

no code implementations • NeurIPS 2018 • Daniel Johnson, Daniel Gorelik, Ross E. Mawhorter, Kyle Suver, Weiqing Gu, Steven Xing, Cody Gabriel, Peter Sankhagowit

We present an approach for simultaneously separating and localizing multiple sound sources using recorded microphone data.

Bayesian Inference Position +1