Variational Inference
748 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
Variational Bayesian Last Layers
We introduce a deterministic variational formulation for training Bayesian last layer neural networks.
Analytical Approximation of the ELBO Gradient in the Context of the Clutter Problem
We propose an analytical solution for approximating the gradient of the Evidence Lower Bound (ELBO) in variational inference problems where the statistical model is a Bayesian network consisting of observations drawn from a mixture of a Gaussian distribution embedded in unrelated clutter, known as the clutter problem.
TrajPRed: Trajectory Prediction with Region-based Relation Learning
We integrate multi-goal estimation and region-based relation learning to model the two stimuli, social interactions, and stochastic goals, in a prediction framework.
VI-OOD: A Unified Representation Learning Framework for Textual Out-of-distribution Detection
Out-of-distribution (OOD) detection plays a crucial role in ensuring the safety and reliability of deep neural networks in various applications.
Variational Stochastic Gradient Descent for Deep Neural Networks
We model gradient updates as a probabilistic model and utilize stochastic variational inference (SVI) to derive an efficient and effective update rule.
SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational Autoencoder
We employ our method to generate paired training samples for real-world image denoising and super-resolution tasks.
An Ordering of Divergences for Variational Inference with Factorized Gaussian Approximations
Our analysis covers the KL divergence, the R\'enyi divergences, and a score-based divergence that compares $\nabla\log p$ and $\nabla\log q$.
Neural Markov Random Field for Stereo Matching
aeolusguan/NMRF
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Stereo matching is a core task for many computer vision and robotics applications.
Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference
As an alternative, we propose SMC-Wake, a procedure for fitting an amortized variational approximation that uses likelihood-tempered sequential Monte Carlo samplers to estimate the gradient of the inclusive KL divergence.
An Efficient Difference-of-Convex Solver for Privacy Funnel
The proposed DC separation results in a closed-form update equation, which allows straightforward application to both known and unknown distribution settings.
