The following pages link to (Q2933843):
Displaying 50 items.
- Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems (Q2125437) (← links)
- An out-of-distribution-aware autoencoder model for reduced chemical kinetics (Q2129157) (← links)
- Stochastic embeddings of dynamical phenomena through variational autoencoders (Q2133707) (← links)
- Variational inference at glacier scale (Q2137912) (← links)
- Variational Bayesian approximation of inverse problems using sparse precision matrices (Q2138759) (← links)
- Stochastic multi-fidelity surrogate modeling of dendritic crystal growth (Q2138818) (← links)
- A machine learning method for real-time numerical simulations of cardiac electromechanics (Q2138843) (← links)
- Statistical challenges in tracking the evolution of SARS-CoV-2 (Q2143932) (← links)
- Large-scale local surrogate modeling of stochastic simulation experiments (Q2157538) (← links)
- Comprehensive analysis of gradient-based hyperparameter optimization algorithms (Q2158645) (← links)
- Variational inference with NoFAS: normalizing flow with adaptive surrogate for computationally expensive models (Q2162034) (← links)
- Probabilistic programming with stochastic variational message passing (Q2169202) (← links)
- Contrastive latent variable modeling with application to case-control sequencing experiments (Q2170381) (← links)
- Fast and accurate variational inference for models with many latent variables (Q2172007) (← links)
- The computational asymptotics of Gaussian variational inference and the Laplace approximation (Q2172111) (← links)
- SHOPPER: a probabilistic model of consumer choice with substitutes and complements (Q2179937) (← links)
- A stochastic variational framework for recurrent Gaussian processes models (Q2188216) (← links)
- Monte Carlo co-ordinate ascent variational inference (Q2195834) (← links)
- \(\alpha\)-variational inference with statistical guarantees (Q2196198) (← links)
- Concentration of tempered posteriors and of their variational approximations (Q2196229) (← links)
- Deep active inference as variational policy gradients (Q2197091) (← links)
- Recursive estimation for sparse Gaussian process regression (Q2203074) (← links)
- Bayesian mean-parameterized nonnegative binary matrix factorization (Q2212539) (← links)
- Noise-free latent block model for high dimensional data (Q2218334) (← links)
- A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the small data regime (Q2222510) (← links)
- Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems (Q2222519) (← links)
- Data-driven, variational model reduction of high-dimensional reaction networks (Q2222683) (← links)
- Densities of almost surely terminating probabilistic programs are differentiable almost everywhere (Q2233472) (← links)
- Simultaneous inference of periods and period-luminosity relations for Mira variable stars (Q2245144) (← links)
- Approximate inference for constructing astronomical catalogs from images (Q2281241) (← links)
- Conditionally conjugate mean-field variational Bayes for logistic models (Q2292397) (← links)
- Solving Bayesian inverse problems from the perspective of deep generative networks (Q2319396) (← links)
- Parametric Gaussian process regression for big data (Q2319397) (← links)
- Cooperative hierarchical Dirichlet processes: superposition vs. maximization (Q2321287) (← links)
- Path-space variational inference for non-equilibrium coarse-grained systems (Q2375138) (← links)
- Collaborative topic regression for online recommender systems: an online and Bayesian approach (Q2398100) (← links)
- Scaling up Bayesian variational inference using distributed computing clusters (Q2411280) (← links)
- A generative model for exploring structure regularities in attributed networks (Q2656752) (← links)
- Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons (Q2681129) (← links)
- Bayesian inference for random field parameters with a goal-oriented quality control of the PGD forward model's accuracy (Q2683299) (← links)
- Discriminative Bayesian filtering lends momentum to the stochastic Newton method for minimizing log-convex functions (Q2693789) (← links)
- On the properties of variational approximations of Gibbs posteriors (Q2958606) (← links)
- Deep Variational Inference (Q3300544) (← links)
- Gaussian Variational Approximation With a Factor Covariance Structure (Q3391080) (← links)
- Bayesian Conditional Density Filtering (Q3391099) (← links)
- Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models (Q3391126) (← links)
- Variational Bayes Estimation of Discrete-Margined Copula Models With Application to Time Series (Q3391262) (← links)
- Inference for the Number of Topics in the Latent Dirichlet Allocation Model via Bayesian Mixture Modeling (Q3391266) (← links)
- Scalable Bayesian Nonparametric Clustering and Classification (Q3391447) (← links)
- Bayesian Deep Net GLM and GLMM (Q3391454) (← links)
