Cover Image

Variational Inference via Smoothed Particle Hydrodynamics

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Huang, Yongchao
Format:	Preprint
Published:	2024
Subjects:	Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Variational Inference Using Material Point Method
by: Huang, Yongchao
Published: (2024)

Bayesian Inference of Training Dataset Membership
by: Huang, Yongchao
Published: (2025)

Electrostatics-based particle sampling and approximate inference
by: Huang, Yongchao
Published: (2024)

Training data membership inference via Gaussian process meta-modeling: a post-hoc analysis approach
by: Huang, Yongchao, et al.
Published: (2025)

Variational Learning Induces Adaptive Label Smoothing
by: Yang, Sin-Han, et al.
Published: (2025)

Learning to be Smooth: An End-to-End Differentiable Particle Smoother
by: Younis, Ali, et al.
Published: (2025)

Learning with Embedded Linear Equality Constraints via Variational Bayesian Inference
by: Marsh, Matthew, et al.
Published: (2026)

Practical Bayesian Inference for Speech SNNs: Uncertainty and Loss-Landscape Smoothing
by: Abdennadher, Yesmine, et al.
Published: (2026)

Time Series Clustering with General State Space Models via Stochastic Variational Inference
by: Ishizuka, Ryoichi, et al.
Published: (2024)

Variational Autoencoders for Efficient Simulation-Based Inference
by: Nautiyal, Mayank, et al.
Published: (2024)

Instance-Adaptive Parametrization for Amortized Variational Inference
by: Pollastro, Andrea, et al.
Published: (2026)

Variational Randomized Smoothing for Sample-Wise Adversarial Robustness
by: Hase, Ryo, et al.
Published: (2024)

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding
by: Lee, Hyomin, et al.
Published: (2025)

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning
by: Winchenbach, Rene, et al.
Published: (2025)

Optimization Guarantees for Square-Root Natural-Gradient Variational Inference
by: Kumar, Navish, et al.
Published: (2025)

Pawsterior: Variational Flow Matching for Structured Simulation-Based Inference
by: Carrasco-Pollo, Jorge, et al.
Published: (2026)

FFHFlow: Diverse and Uncertainty-Aware Dexterous Grasp Generation via Flow Variational Inference
by: Feng, Qian, et al.
Published: (2024)

Brain-like Variational Inference
by: Vafaii, Hadi, et al.
Published: (2024)

DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing
by: Lee, Vint, et al.
Published: (2023)

Composing Non-Conjugate Factor Graphs with Closed-Form Variational Inference
by: Lukashchuk, Mykola, et al.
Published: (2026)

Partially Observable Gaussian Process Network and Doubly Stochastic Variational Inference
by: Kiroriwal, Saksham, et al.
Published: (2025)

Variational Causal Inference
by: Wu, Yulun, et al.
Published: (2022)

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods
by: Puri, Isha, et al.
Published: (2025)

Probabilistic Programming with Programmable Variational Inference
by: Becker, McCoy R., et al.
Published: (2024)

Smoothed Graph Contrastive Learning via Seamless Proximity Integration
by: Behmanesh, Maysam, et al.
Published: (2024)

Certified Adversarial Robustness via Partition-based Randomized Smoothing
by: Goli, Hossein, et al.
Published: (2024)

Fast and Robust Likelihood-Guided Diffusion Posterior Sampling with Amortized Variational Inference
by: Zheng, Léon, et al.
Published: (2026)

Aligning Few-Step Generative Models by Amortizing Sample-based Variational Inference
by: Lee, Jaewoo, et al.
Published: (2026)

Calibrated Language Models and How to Find Them with Label Smoothing
by: Huang, Jerry, et al.
Published: (2025)

Fast Adversarial Training against Sparse Attacks Requires Loss Smoothing
by: Zhong, Xuyang, et al.
Published: (2025)

MAVRL: Learning Reward Functions from Multiple Feedback Types with Amortized Variational Inference
by: Baur, Raphaël, et al.
Published: (2026)

Masked Auto-Regressive Variational Acceleration: Fast Inference Makes Practical Reinforcement Learning
by: Gu, Yuxuan, et al.
Published: (2025)

Certified Robustness for Deep Equilibrium Models via Serialized Random Smoothing
by: Gao, Weizhi, et al.
Published: (2024)

Counterfactual Generative Modeling with Variational Causal Inference
by: Wu, Yulun, et al.
Published: (2024)

Score-Based Variational Inference for Inverse Problems
by: Xue, Zhipeng, et al.
Published: (2024)

Reconcile Certified Robustness and Accuracy for DNN-based Smoothed Majority Vote Classifier
by: Jin, Gaojie, et al.
Published: (2025)

Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference
by: Xu, Jian, et al.
Published: (2024)

CluCERT: Certifying LLM Robustness via Clustering-Guided Denoising Smoothing
by: Wang, Zixia, et al.
Published: (2025)

Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs
by: Wu, Xiabao, et al.
Published: (2025)

Adaptive Variational Inference in Probabilistic Graphical Models: Beyond Bethe, Tree-Reweighted, and Convex Free Energies
by: Leisenberger, Harald, et al.
Published: (2025)