Search Results for author:
Found 6 papers, 3 papers with code
American postdoctoral salaries do not account for growing disparities in cost of living
More than 27, 000 postdoc salaries across all US universities are analyzed alongside measures of regional differences in cost of living.
A Primer on Deep Learning for Causal Inference
This review systematizes the emerging literature for causal inference using deep neural networks under the potential outcomes framework.
Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning
We propose Parametric UMAP, a parametric variation of the UMAP (Uniform Manifold Approximation and Projection) algorithm.
Parametric UMAP embeddings for representation and semi-supervised learning
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.
Generative adversarial interpolative autoencoding: adversarial training on latent space interpolations encourages convex latent distributions
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.
Generative adversarial interpolative autoencoding: adversarial training on latent space interpolations encourage convex latent distributions
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.
