The following pages link to (Q4998956):
Displaying 50 items.
- Probabilistic Time Series Forecasts with Autoregressive Transformation Models (Q88049) (← links)
- Normalizing flow policies for multi-agent systems (Q2056951) (← links)
- Particle-based energetic variational inference (Q2058750) (← links)
- \textsc{Strudel}: A fast and accurate learner of structured-decomposable probabilistic circuits (Q2069054) (← links)
- Analyzing stochastic computer models: a review with opportunities (Q2075795) (← links)
- Stochastic normalizing flows as non-equilibrium transformations (Q2104579) (← links)
- Variational inference with vine copulas: an efficient approach for Bayesian computer model calibration (Q2110192) (← links)
- Low-rank tensor reconstruction of concentrated densities with application to Bayesian inversion (Q2128063) (← links)
- A hybrid stochastic model and its Bayesian identification for infectious disease screening in a university campus with application to massive COVID-19 screening at the university of Liège (Q2147416) (← links)
- Sparse approximation of triangular transports. I: The finite-dimensional case (Q2672289) (← links)
- Sparse approximation of triangular transports. II: The infinite-dimensional case (Q2672290) (← links)
- (Q5054640) (← links)
- Solving Inverse Problems by Joint Posterior Maximization with Autoencoding Prior (Q5094620) (← links)
- Fast and credible likelihood-free cosmology with truncated marginal neural ratio estimation (Q5104215) (← links)
- Solving Time Dependent Fokker-Planck Equations via Temporal Normalizing Flow (Q5106295) (← links)
- Quasipolynomial Normalisation in Deep Inference via Atomic Flows and Threshold Formulae (Q5739889) (← links)
- Deep Learning for Image/Video Restoration and Super-resolution (Q5863770) (← links)
- Turnpike in optimal control of PDEs, ResNets, and beyond (Q5887835) (← links)
- Neural spline flow multi-constraint NURBS method for three-dimensional automatic geological modeling with multiple constraints (Q6074253) (← links)
- Adaptive deep density approximation for fractional Fokker-Planck equations (Q6087826) (← links)
- Reservoir computing with error correction: long-term behaviors of stochastic dynamical systems (Q6090663) (← links)
- Sparsity in long-time control of neural ODEs (Q6099693) (← links)
- Black Box Variational Bayesian Model Averaging (Q6100009) (← links)
- Contextual movement models based on normalizing flows (Q6107401) (← links)
- Analysis of a Class of Multilevel Markov Chain Monte Carlo Algorithms Based on Independent Metropolis–Hastings (Q6109156) (← links)
- Stein variational gradient descent with learned direction (Q6124701) (← links)
- Koopman operator learning using invertible neural networks (Q6126575) (← links)
- Variational inference in neural functional prior using normalizing flows: application to differential equation and operator learning problems (Q6132292) (← links)
- MapFlow: latent transition via normalizing flow for unsupervised domain adaptation (Q6134356) (← links)
- Entropy estimation via uniformization (Q6136099) (← links)
- Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data and machine learning surrogate (Q6147036) (← links)
- Scalable conditional deep inverse Rosenblatt transports using tensor trains and gradient-based dimension reduction (Q6158090) (← links)
- Variational inference for cutting feedback in misspecified models (Q6181748) (← links)
- A machine learning framework for geodesics under spherical Wasserstein-Fisher-Rao metric and its application for weighted sample generation (Q6184267) (← links)
- Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy (Q6185714) (← links)
- A dimension-reduced variational approach for solving physics-based inverse problems using generative adversarial network priors and normalizing flows (Q6194145) (← links)
- Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows (Q6491836) (← links)
- Emerging directions in Bayesian computation (Q6540230) (← links)
- Model-based reinforcement learning with non-Gaussian environment dynamics and its application to portfolio optimization (Q6550745) (← links)
- Mixture modeling with normalizing flows for spherical density estimation (Q6552950) (← links)
- NF-ULA: normalizing flow-based unadjusted Langevin algorithm for imaging inverse problems (Q6556790) (← links)
- Dynamic CVaR portfolio construction with attention-powered generative factor learning (Q6558580) (← links)
- Energetic variational neural network discretizations of gradient flows (Q6585315) (← links)
- Conditional sampling with monotone GANs: from generative models to likelihood-free inference (Q6587628) (← links)
- Improved multifidelity Monte Carlo estimators based on normalizing flows and dimensionality reduction techniques (Q6588269) (← links)
- Principal feature detection via \(\phi \)-Sobolev inequalities (Q6589581) (← links)
- Optimal experimental design: formulations and computations (Q6598420) (← links)
- A pseudoreversible normalizing flow for stochastic dynamical systems with various initial distributions (Q6598493) (← links)
- Returning CP-observables to the frames they belong (Q6607150) (← links)
- An introduction to quantum computing for statisticians and data scientists (Q6620130) (← links)
