1 code implementation • 21 Sep 2022 • Eric Yeats, Frank Liu, David Womble, Hai Li
We present a self-supervised method to disentangle factors of variation in high-dimensional data that does not rely on prior knowledge of the underlying variation profile (e. g., no assumptions on the number or distribution of the individual latent variables to be extracted).
1 code implementation • 15 Oct 2019 • Theodore Papamarkou, Jacob Hinkle, M. Todd Young, David Womble
Nevertheless, this paper shows that a non-converged Markov chain, generated via MCMC sampling from the parameter space of a neural network, can yield via Bayesian marginalization a valuable posterior predictive distribution of the output of the neural network.