REVIEW 3 cited by
Kernel Implicit Variational Inference
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Recent progress in variational inference has paid much attention to the flexibility of variational posteriors. One promising direction is to use implicit distributions, i.e., distributions without tractable densities as the variational posterior. However, existing methods on implicit posteriors still face challenges of noisy estimation and computational infeasibility when applied to models with high-dimensional latent variables. In this paper, we present a new approach named Kernel Implicit Variational Inference that addresses these challenges. As far as we know, for the first time implicit variational inference is successfully applied to Bayesian neural networks, which shows promising results on both regression and classification tasks.
Forward citations
Cited by 3 Pith papers
-
Enhancing Uncertainty Estimation and Interpretability via Bayesian Non-negative Decision Layer
A Bayesian non-negative decision layer with gamma priors and Weibull variational inference improves uncertainty estimation and interpretability for image classifiers.
-
A Cubing Strategy for Identifying Stable Hyperparameter Regions for Uncertainty Quantification in Spatial Deep Learning
A recursive cubing framework identifies stable hyperparameter regions for MC dropout uncertainty quantification in spatial deep learning and produces competitive or superior predictive intervals versus a statistical b...
-
Importance Weighted Score Matching for Diffusion Samplers with Enhanced Mode Coverage
Importance Weighted Score Matching trains diffusion samplers by reweighting score matching with self-normalized importance sampling to approximate the forward KL and improve mode coverage.
Discussion (0). Sign in to comment.