:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Bartosh, Grigory, Vetrov, Dmitry, Naesseth, Christian A.
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2502.02472
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Neural Diffusion Models
by: Bartosh, Grigory, et al.
Published: (2023)

Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling
by: Bartosh, Grigory, et al.
Published: (2024)

Towards Latent Diffusion Suitable For Text
by: Midavaine, Nesta, et al.
Published: (2026)

Equivariant Neural Diffusion for Molecule Generation
by: Cornet, François, et al.
Published: (2025)

Variational Flow Matching for Graph Generation
by: Eijkelboom, Floor, et al.
Published: (2024)

SGD as Free Energy Minimization: A Thermodynamic View on Neural Network Training
by: Sadrtdinov, Ildus, et al.
Published: (2025)

Forward-Learned Discrete Diffusion: Learning how to noise to denoise faster
by: Bartosh, Grigory, et al.
Published: (2026)

Regularized Distribution Matching Distillation for One-step Unpaired Image-to-Image Translation
by: Rakitin, Denis, et al.
Published: (2024)

Efficient Training of Neural Stochastic Differential Equations by Matching Finite Dimensional Distributions
by: Zhang, Jianxin, et al.
Published: (2024)

Precise: SDE-Consistent Stochastic Sampling for RL Post-Training of Flow-Matching Models
by: Zou, Jade, et al.
Published: (2026)

Flow Matching for Tabular Data Synthesis
by: Nasution, Bahrul Ilmi, et al.
Published: (2025)

Neural Stochastic Flows: Solver-Free Modelling and Inference for SDE Solutions
by: Kiyohara, Naoki, et al.
Published: (2025)

Improving the Noise Estimation of Latent Neural Stochastic Differential Equations
by: Heck, Linus, et al.
Published: (2024)

Latent Stochastic Interpolants
by: Singh, Saurabh, et al.
Published: (2025)

SDE-Attention: Latent Attention in SDE-RNNs for Irregularly Sampled Time Series with Missing Data
by: Fang, Yuting, et al.
Published: (2025)

A Geometric Approach to Optimal Experimental Design
by: Kerrigan, Gavin, et al.
Published: (2025)

Generative Modeling of Neural Dynamics via Latent Stochastic Differential Equations
by: ElGazzar, Ahmed, et al.
Published: (2024)

Dual-Rate Diffusion: Accelerating diffusion models with an interleaved heavy-light network
by: Bartosh, Grigory, et al.
Published: (2026)

To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning
by: Sadrtdinov, Ildus, et al.
Published: (2023)

Generative Flow Networks as Entropy-Regularized RL
by: Tiapkin, Daniil, et al.
Published: (2023)

Hierarchical Stochastic Differential Equation Models for Latent Manifold Learning in Neural Time Series
by: Rajaei, Pedram, et al.
Published: (2025)

Generative Modeling of Clinical Time Series via Latent Stochastic Differential Equations
by: Aslanimoghanloo, Muhammad, et al.
Published: (2025)

Streaming Generation of Co-Speech Gestures via Accelerated Rolling Diffusion
by: Vu, Evgeniia, et al.
Published: (2025)

Elements of Sequential Monte Carlo
by: Naesseth, Christian A., et al.
Published: (2019)

Latent Mamba Operator for Partial Differential Equations
by: Tiwari, Karn, et al.
Published: (2025)

On Continuous Monitoring of Risk Violations under Unknown Shift
by: Timans, Alexander, et al.
Published: (2025)

E-Valuating Classifier Two-Sample Tests
by: Pandeva, Teodora, et al.
Published: (2022)

Where Do Large Learning Rates Lead Us?
by: Sadrtdinov, Ildus, et al.
Published: (2024)

Controlled Generation with Equivariant Variational Flow Matching
by: Eijkelboom, Floor, et al.
Published: (2025)

Efficient Neural SDE Training using Wiener-Space Cubature
by: Snow, Luke, et al.
Published: (2025)

HGAN-SDEs: Learning Neural Stochastic Differential Equations with Hermite-Guided Adversarial Training
by: Xu, Yuanjian, et al.
Published: (2025)

VISA: Variational Inference with Sequential Sample-Average Approximations
by: Zimmermann, Heiko, et al.
Published: (2024)

Monitoring Risks in Test-Time Adaptation
by: Schirmer, Mona, et al.
Published: (2025)

Towards Identifiability of Interventional Stochastic Differential Equations
by: Zweig, Aaron, et al.
Published: (2025)

Improving GFlowNets with Monte Carlo Tree Search
by: Morozov, Nikita, et al.
Published: (2024)

Purrception: Variational Flow Matching for Vector-Quantized Image Generation
by: Matişan, Răzvan-Andrei, et al.
Published: (2025)

Variational Neural Stochastic Differential Equations with Change Points
by: El-Laham, Yousef, et al.
Published: (2024)

Stochastic Differential Equations models for Least-Squares Stochastic Gradient Descent
by: Schertzer, Adrien, et al.
Published: (2024)

Neural Structure Learning with Stochastic Differential Equations
by: Wang, Benjie, et al.
Published: (2023)

PSyDUCK: Training-Free Steganography for Latent Diffusion
by: Mahfuz, Aqib, et al.
Published: (2025)