Variational Inference
753 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.
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Latest papers with no code
Variational Schrödinger Diffusion Models
To improve the scalability while preserving efficient transportation plans, we leverage variational inference to linearize the forward score functions (variational scores) of SB and restore simulation-free properties in training backward scores.
Scalable Amortized GPLVMs for Single Cell Transcriptomics Data
Dimensionality reduction is crucial for analyzing large-scale single-cell RNA-seq data.
Few-sample Variational Inference of Bayesian Neural Networks with Arbitrary Nonlinearities
In this work, we demonstrate a simple yet effective approach for propagating statistical moments through arbitrary nonlinearities with only 3 deterministic samples, enabling few-sample variational inference of BNNs without restricting the set of network layers used.
floZ: Evidence estimation from posterior samples with normalizing flows
We propose a novel method (floZ), based on normalizing flows, for estimating the Bayesian evidence (and its numerical uncertainty) from a set of samples drawn from the unnormalized posterior distribution.
Calibrating Bayesian Learning via Regularization, Confidence Minimization, and Selective Inference
This paper proposes an extension of variational inference (VI)-based Bayesian learning that integrates calibration regularization for improved ID performance, confidence minimization for OOD detection, and selective calibration to ensure a synergistic use of calibration regularization and confidence minimization.
Sampling for Model Predictive Trajectory Planning in Autonomous Driving using Normalizing Flows
In this context, normalizing flows originating from the field of variational inference are considered for the generation of sampling distributions, as they model transformations of simple to more complex distributions.
Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control
Variational inference is used by NSVB-MPC to assess the predictive accuracy and make the necessary corrections to quantify system uncertainty.
Extending Mean-Field Variational Inference via Entropic Regularization: Theory and Computation
Variational inference (VI) has emerged as a popular method for approximate inference for high-dimensional Bayesian models.
Convergence of coordinate ascent variational inference for log-concave measures via optimal transport
Mean field variational inference (VI) is the problem of finding the closest product (factorized) measure, in the sense of relative entropy, to a given high-dimensional probability measure $\rho$.
Preventing Model Collapse in Gaussian Process Latent Variable Models
Gaussian process latent variable models (GPLVMs) are a versatile family of unsupervised learning models, commonly used for dimensionality reduction.
