Variational Inference
738 papers with code • 1 benchmarks • 5 datasets
Fitting approximate posteriors with variational inference transforms the inference problem into an optimization problem, where the goal is (typically) to optimize the evidence lower bound (ELBO) on the log likelihood of the data.
Libraries
Use these libraries to find Variational Inference models and implementationsMost implemented papers
Doubly Stochastic Variational Inference for Deep Gaussian Processes
Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice.
Variational Continual Learning
This paper develops variational continual learning (VCL), a simple but general framework for continual learning that fuses online variational inference (VI) and recent advances in Monte Carlo VI for neural networks.
Neural Spline Flows
A normalizing flow models a complex probability density as an invertible transformation of a simple base density.
FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models
The result is a continuous-time invertible generative model with unbiased density estimation and one-pass sampling, while allowing unrestricted neural network architectures.
Neural Variational Inference for Text Processing
We validate this framework on two very different text modelling applications, generative document modelling and supervised question answering.
Variational Inference: A Review for Statisticians
One of the core problems of modern statistics is to approximate difficult-to-compute probability densities.
Autoencoding Variational Inference For Topic Models
A promising approach to address this problem is autoencoding variational Bayes (AEVB), but it has proven diffi- cult to apply to topic models in practice.
Variational Approaches for Auto-Encoding Generative Adversarial Networks
In this paper, we develop a principle upon which auto-encoders can be combined with generative adversarial networks by exploiting the hierarchical structure of the generative model.
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
In this paper, Bayesian Convolutional Neural Network (BayesCNN) using Variational Inference is proposed, that introduces probability distribution over the weights.
Multi-Object Representation Learning with Iterative Variational Inference
Human perception is structured around objects which form the basis for our higher-level cognition and impressive systematic generalization abilities.