1 code implementation • 25 May 2022 • Tim Sainburg
More than 27, 000 postdoc salaries across all US universities are analyzed alongside measures of regional differences in cost of living.
no code implementations • 9 Oct 2021 • Bernard Koch, Tim Sainburg, Pablo Geraldo, Song Jiang, Yizhou Sun, Jacob Gates Foster
This review systematizes the emerging literature for causal inference using deep neural networks under the potential outcomes framework.
no code implementations • 28 Sep 2020 • Tim Sainburg, Leland McInnes, Timothy Q Gentner
We propose Parametric UMAP, a parametric variation of the UMAP (Uniform Manifold Approximation and Projection) algorithm.
2 code implementations • 27 Sep 2020 • 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 data.
no code implementations • 27 Sep 2018 • Tim Sainburg, Marvin Thielk, Brad Thielman, Benjamin Migliori, Timothy Gentner
We present a neural network architecture based upon the Autoencoder (AE) and Generative Adversarial Network (GAN) that promotes a convex latent distribution by training adversarially on latent space interpolations.
1 code implementation • 17 Jul 2018 • Tim Sainburg, Marvin Thielk, Brad Theilman, Benjamin Migliori, Timothy Gentner
We present a neural network architecture based upon the Autoencoder (AE) and Generative Adversarial Network (GAN) that promotes a convex latent distribution by training adversarially on latent space interpolations.