Neural Networks with Cheap Differential Operators

no code implementations • NeurIPS 2019 • Tian Qi Chen, David K. Duvenaud

Gradients of neural networks can be computed efficiently for any architecture, but some applications require computing differential operators with higher time complexity.

Latent Ordinary Differential Equations for Irregularly-Sampled Time Series

1 code implementation • NeurIPS 2019 • Yulia Rubanova, Tian Qi Chen, David K. Duvenaud

Time series with non-uniform intervals occur in many applications, and are difficult to model using standard recurrent neural networks (RNNs).

Time Series Time Series Analysis

Fast Patch-based Style Transfer of Arbitrary Style

6 code implementations • 13 Dec 2016 • Tian Qi Chen, Mark Schmidt

This results in a procedure for artistic style transfer that is efficient but also allows arbitrary content and style images.

Image Generation Style Transfer