Copertina

Model-Informed Flows for Bayesian Inference

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ko, Joohwan, Domke, Justin
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Amortized Factor Inference Networks for Posterior Inference
di: Ko, Joohwan, et al.
Pubblicazione: (2026)

Latent Target Score Matching, with an application to Simulation-Based Inference
di: Ko, Joohwan, et al.
Pubblicazione: (2026)

Large Language Bayes
di: Domke, Justin
Pubblicazione: (2025)

Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models
di: Lai, Jinlin, et al.
Pubblicazione: (2024)

Modeling Choice via Self-Attention
di: Ko, Joohwan, et al.
Pubblicazione: (2023)

Disentangling impact of capacity, objective, batchsize, estimators, and step-size on flow VI
di: Agrawal, Abhinav, et al.
Pubblicazione: (2024)

Understanding and mitigating difficulties in posterior predictive evaluation
di: Agrawal, Abhinav, et al.
Pubblicazione: (2024)

Provably Scalable Black-Box Variational Inference with Structured Variational Families
di: Ko, Joohwan, et al.
Pubblicazione: (2024)

Joint control variate for faster black-box variational inference
di: Wang, Xi, et al.
Pubblicazione: (2022)

Layer-Adaptive State Pruning for Deep State Space Models
di: Gwak, Minseon, et al.
Pubblicazione: (2024)

Demystifying SGD with Doubly Stochastic Gradients
di: Kim, Kyurae, et al.
Pubblicazione: (2024)

Simulation-based stacking
di: Yao, Yuling, et al.
Pubblicazione: (2023)

Relaxed Sequence Sampling for Diverse Protein Design
di: Ko, Joohwan, et al.
Pubblicazione: (2025)

Conditional Flow Matching for Bayesian Posterior Inference
di: Zhai, Percy S., et al.
Pubblicazione: (2025)

Physics-Informed Sylvester Normalizing Flows for Bayesian Inference in Magnetic Resonance Spectroscopy
di: Merkofer, Julian P., et al.
Pubblicazione: (2025)

Supercharging Bayesian Inference with Reliable AI-Informed Priors
di: Choi, Jongwoo, et al.
Pubblicazione: (2026)

Scalable Inference for Bayesian Multinomial Logistic-Normal Dynamic Linear Models
di: Saxena, Manan, et al.
Pubblicazione: (2024)

Normalizing Flow Regression for Bayesian Inference with Offline Likelihood Evaluations
di: Li, Chengkun, et al.
Pubblicazione: (2025)

Riemannian Diffusion Models on General Manifolds via Physics-Informed Neural Networks
di: Ko, Gyeonghoon, et al.
Pubblicazione: (2026)

Enhancing Topological Dependencies in Spatio-Temporal Graphs with Cycle Message Passing Blocks
di: Lee, Minho, et al.
Pubblicazione: (2024)

WINA: Weight Informed Neuron Activation for Accelerating Large Language Model Inference
di: Chen, Sihan, et al.
Pubblicazione: (2025)

Generative Modeling with Bayesian Sample Inference
di: Lienen, Marten, et al.
Pubblicazione: (2025)

The Practicality of Normalizing Flow Test-Time Training in Bayesian Inference for Agent-Based Models
di: Zhang, Junyao, et al.
Pubblicazione: (2026)

BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Python
di: Kühmichel, Lars, et al.
Pubblicazione: (2026)

Learning to Scale Logits for Temperature-Conditional GFlowNets
di: Kim, Minsu, et al.
Pubblicazione: (2023)

LPDP: Inference-Time Reward Control for Variable-Length DNA Generation with Edit Flows
di: Kim, Jeongchan, et al.
Pubblicazione: (2026)

Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models
di: Fischer, John, et al.
Pubblicazione: (2024)

Uncertainty Quantification in PINNs for Turbulent Flows: Bayesian Inference and Repulsive Ensembles
di: Shukla, Khemraj, et al.
Pubblicazione: (2026)

Towards Improved Variational Inference for Deep Bayesian Models
di: Ober, Sebastian W.
Pubblicazione: (2024)

Statistical Inference via Generative Models: Flow Matching and Causal Inference
di: Eguchi, Shinto
Pubblicazione: (2026)

Forecasting at Full Spectrum: Holistic Multi-Granular Traffic Modeling under High-Throughput Inference Regimes
di: Wang, Zhaoyan, et al.
Pubblicazione: (2025)

Federated Variational Inference for Bayesian Mixture Models
di: Rao, Jackie, et al.
Pubblicazione: (2025)

Bayesian Federated Inference for Survival Models
di: Pazira, Hassan, et al.
Pubblicazione: (2024)

Bayesian Prediction-Powered Inference
di: Hofer, R. Alex, et al.
Pubblicazione: (2024)

Bayesian Inverse Problems Meet Flow Matching: Efficient and Flexible Inference via Transformers
di: Sherki, Daniil, et al.
Pubblicazione: (2025)

Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Models
di: Vetter, Julius, et al.
Pubblicazione: (2025)

Corrected Integrated Laplace Approximation for Bayesian Inference in Latent Gaussian Models
di: Lai, Jinlin, et al.
Pubblicazione: (2026)

Bayesian Semi-structured Subspace Inference
di: Dold, Daniel, et al.
Pubblicazione: (2024)

Transformers Can Do Bayesian Inference
di: Müller, Samuel, et al.
Pubblicazione: (2021)

Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review
di: Han, Tao, et al.
Pubblicazione: (2025)