Pages that link to "Item:Q2855953"

The following pages link to Machine learning. A probabilistic perspective (Q2855953):

Displaying 50 items.

• Estimating the conditional distribution in functional regression problems (Q2106779) (← links)
• Quantum approximate optimization algorithm for Bayesian network structure learning (Q2111010) (← links)
• Bayesian estimation of constrained mean-covariance of normal distributions (Q2112272) (← links)
• Robust optimization of attenuation bands of three-dimensional periodic frame structures (Q2115425) (← links)
• Tool path optimization of selective laser sintering processes using deep learning (Q2115582) (← links)
• A minimal cardinality solution to fitting sawtooth piecewise-linear functions (Q2116611) (← links)
• ProCount: weighted projected model counting with graded project-join trees (Q2118296) (← links)
• Hierarchical sparse observation models and informative prior for Bayesian inference of spatially varying parameters (Q2123799) (← links)
• Gaussian process regression for maximum entropy distribution (Q2124600) (← links)
• Gaussian process repetitive control: beyond periodic internal models through kernels (Q2125567) (← links)
• A comparative study of machine learning models for predicting the state of reactive mixing (Q2128488) (← links)
• Stochastic embeddings of dynamical phenomena through variational autoencoders (Q2133707) (← links)
• State estimation with limited sensors -- a deep learning based approach (Q2135833) (← links)
• Probabilistic learning inference of boundary value problem with uncertainties based on Kullback-Leibler divergence under implicit constraints (Q2142219) (← links)
• Hierarchical Bayesian text modeling for the unsupervised joint analysis of latent topics and semantic clusters (Q2152511) (← links)
• Parameter inference in a probabilistic model using clustered data (Q2155048) (← links)
• Bayesian nonparametric learning of how skill is distributed across the mutual fund industry (Q2155311) (← links)
• Improving sequential latent variable models with autoregressive flows (Q2163209) (← links)
• Stream-based active learning for sliding windows under the influence of verification latency (Q2163242) (← links)
• Estimation of distribution algorithms using Gaussian Bayesian networks to solve industrial optimization problems constrained by environment variables (Q2165268) (← links)
• A FastMap-based algorithm for block modeling (Q2170199) (← links)
• The computational asymptotics of Gaussian variational inference and the Laplace approximation (Q2172111) (← links)
• Rejoinder on: ``On active learning methods for manifold data'' (Q2177725) (← links)
• Data-driven modelling of the Reynolds stress tensor using random forests with invariance (Q2180004) (← links)
• A comparison of economic agent-based model calibration methods (Q2181534) (← links)
• Optimizing predictive precision in imbalanced datasets for actionable revenue change prediction (Q2184072) (← links)
• Stress-based topology optimization under uncertainty via simulation-based Gaussian process (Q2184305) (← links)
• Hyperparameter estimation in Bayesian MAP estimation: parameterizations and consistency (Q2188103) (← links)
• An alternative to EM for Gaussian mixture models: batch and stochastic Riemannian optimization (Q2188245) (← links)
• A preference learning framework for multiple criteria sorting with diverse additive value models and valued assignment examples (Q2189891) (← links)
• A branch-and-bound framework for unsupervised common event discovery (Q2193773) (← links)
• A comparison study of similarity measures for covering-based neighborhood classifiers (Q2195335) (← links)
• A Bayesian perspective of statistical machine learning for big data (Q2203387) (← links)
• Sampling of Bayesian posteriors with a non-Gaussian probabilistic learning on manifolds from a small dataset (Q2209715) (← links)
• Modeling frequency and severity of claims with the zero-inflated generalized cluster-weighted models (Q2212142) (← links)
• A survey on HHL algorithm: from theory to application in quantum machine learning (Q2213223) (← links)
• Regularized greedy column subset selection (Q2215118) (← links)
• Entropy-based closure for probabilistic learning on manifolds (Q2220629) (← links)
• Adaptive non-intrusive reduced order modeling for compressible flows (Q2222527) (← links)
• Monetary policy rules in a non-rational world: a macroeconomic experiment (Q2231402) (← links)
• Collocation based training of neural ordinary differential equations (Q2236696) (← links)
• Probabilistic learning on manifolds constrained by nonlinear partial differential equations for small datasets (Q2236928) (← links)
• Some thoughts on knowledge-enhanced machine learning (Q2237522) (← links)
• Deciding when to quit the gambler's ruin game with unknown probabilities (Q2237528) (← links)
• Statistical interpolation of spatially varying but sparsely measured 3D geo-data using compressive sensing and variational Bayesian inference (Q2238104) (← links)
• Making sense of sensory input (Q2238610) (← links)
• The role of surrogate models in the development of digital twins of dynamic systems (Q2241783) (← links)
• Equivalence class selection of categorical graphical models (Q2242176) (← links)
• Instance-dependent cost-sensitive learning for detecting transfer fraud (Q2242220) (← links)
• Partitioning some multivariate distributions (Q2244850) (← links)