43 citations · 62 across the 4 of their papers we have counts for
3 papers · 1 filter
A Primer on Deep Learning for Causal Inference
Bernard Koch, Tim Sainburg, Pablo Geraldo +3
This review systematizes the emerging literature for causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction on ho…
Parametric UMAP embeddings for representation and semi-supervised learning
Tim Sainburg, Leland McInnes, Timothy Q Gentner
UMAP is a non-parametric graph-based dimensionality reduction algorithm using applied Riemannian geometry and algebraic topology to find low-dimensional embeddings of structured da…
Generative adversarial interpolative autoencoding: adversarial training on latent space interpolations encourage convex latent distributions
Tim Sainburg, Marvin Thielk, Brad Theilman +2
We present a neural network architecture based upon the Autoencoder (AE) and Generative Adversarial Network (GAN) that promotes a convex latent distribution by training adversarial…