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
VISA: Variational Inference with Sequential Sample-Average Approximations
We present variational inference with sequential sample-average approximation (VISA), a method for approximate inference in computationally intensive models, such as those based on numerical simulations.
Nonparametric Automatic Differentiation Variational Inference with Spline Approximation
Compared with widely-used nonparametric variational inference methods, the proposed method is easy to implement and adaptive to various data structures.
Variational Inference of Parameters in Opinion Dynamics Models
We validate our method on a bounded confidence model with agent roles (leaders and followers).
Large-scale variational Gaussian state-space models
We introduce an amortized variational inference algorithm and structured variational approximation for state-space models with nonlinear dynamics driven by Gaussian noise.
Language-guided Skill Learning with Temporal Variational Inference
We present an algorithm for skill discovery from expert demonstrations.
Re-Envisioning Numerical Information Field Theory (NIFTy.re): A Library for Gaussian Processes and Variational Inference
Imaging is the process of transforming noisy, incomplete data into a space that humans can interpret.
Accelerating Convergence of Stein Variational Gradient Descent via Deep Unfolding
Stein variational gradient descent (SVGD) is a prominent particle-based variational inference method used for sampling a target distribution.
A Framework for Variational Inference of Lightweight Bayesian Neural Networks with Heteroscedastic Uncertainties
Obtaining heteroscedastic predictive uncertainties from a Bayesian Neural Network (BNN) is vital to many applications.
Bayesian Neural Networks with Domain Knowledge Priors
Bayesian neural networks (BNNs) have recently gained popularity due to their ability to quantify model uncertainty.
Variational Entropy Search for Adjusting Expected Improvement
Bayesian optimization is a widely used technique for optimizing black-box functions, with Expected Improvement (EI) being the most commonly utilized acquisition function in this domain.
