Continuous Normalizing Flows

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1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Seq-Flow: Efficient Probabilistic Forecasting with Self-Rollout Error Control
Oct 7, 2026Yinan Huang, Shitij Govil, Bo Dai +1Probabilistic Forecasting Probabilistic Time Series Forecasting
Georgia Institute of Technology
Off-manifold robustness in synthesizer inversion with joint distribution flow matching
Sep 24, 2026Ben HayesSynthetic-to-Real Domain Adaptation Flow Matching
Sony Computer Science Laboratories Paris
Learning Informative Prior with Infinite-Dimensional Continuous Normalizing Flow for Bayesian Inverse Problem
Sep 3, 2026Yang Zhao, Junxiong Jia, Tao ZhouNormalizing Flows Bayesian Inverse Problems
School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, China · Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing, 100190, China
CQF-HMR: Continuous Quaternion Flows for Probabilistic 3D Human Mesh Recovery from a Single Image
Sep 1, 2026Cuong Le, Bao-Long Tran, Pavlo Melnyk +3Human Mesh Recovery 3D Human Pose Estimation
Linköping University · Independent researcher.
Potential Matching Optimal Transport: Continuous Normalizing Flows for Exact $p$ -Wasserstein Dynamics
Aug 6, 2026Lishuo Zhang, Ruizhi Huang, Yang Yu +1Flow Matching Continuous Normalizing Flows
School of Mathematical Sciences, Shanghai Jiao Tong University · Institute of Natural Sciences, MOE-LSC, Shanghai Jiao Tong University
Joint Flow Matching for Generator-Consistent Classification
Jul 27, 2026Hayden McAlister, Lech SzymanskiFlow Matching Continuous Normalizing Flows
School of Computing University of Otago Dunedin, New Zealand
Stability of Flow Models for Graph Signals
Jul 8, 2026Martin Schmidt, Gonzalo MateosGraph Signal Processing Flow Matching
Dept. of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627, USA
Self-conditioned Flow Map Language Models via Fixed-point Flows
Jul 1, 2026Jaehoon Yoo, Wonjung Kim, Floor Eijkelboom +4Normalizing Flows One-Step Generative Modeling
KAIST · University of Amsterdam · Carnegie Mellon University
Understanding Latent Flow Models for Tabular Data Synthesis: Targets, Paths, and Sampling
Jun 18, 2026Bahrul Ilmi NasutionSynthetic Tabular Data Generation Continuous Normalizing Flows
Department of Social Statistics, The University of Manchester, UK
Low-Pass Flow Matching
Jun 1, 2026Francesco M. Ruscio, T. Konstantin RuschFlow Matching Image Generation
ELLIS Institute Tübingen & · Max Planck Institute for Intelligent Systems & · AITHYRA +2
Local Diagnostics of Continuous Normalizing Flow for Out-of-Distribution Detection
May 30, 2026Xinwei Cao, Mengxuan Lu, Torbjørn Svendsen +1Speech Processing Distribution Shift Detection
Department of Electronic Systems Norwegian University of Science and Technology Trondheim, Norway · School of Future Technology South China University of Technology Guangzhou, China
Parameter-Efficient Generative Modeling with Controlled Vector Fields
May 27, 2026Peyman MortezaGenerative Modeling Continuous Normalizing Flows
Two-Parameter Flows for Learning Population Dynamics of Physical Systems
May 25, 2026Paul Schwerdtner, Tobias Blickhan, Benjamin PeherstorferDynamical Systems Continuous Normalizing Flows
Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY 10012, USA.
StAD: Stein Amortized Divergence for Fast Likelihoods with Diffusion and Flow
May 15, 2026Gurjeet Jagwani, Stephen Thorp, Sinan Deger +1Continuous Normalizing Flows Flow-Based Generative Modeling
Institute of Astronomy and Kavli Institute for Cosmology, University of Cambridge, Cambridge, UK · Research Computing Services, University of Cambridge, Cambridge, UK · Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, UK +1
Language Modeling with Hyperspherical Flows
May 11, 2026Justin Deschenaux, Caglar GulcehreNormalizing Flows Continuous Normalizing Flows
EPFL · Lausanne, Switzerland · EPFL, Lausanne, Switzerland +1
CoreFlow: Low-Rank Matrix Generative Models
Apr 27, 2026Dongze Wu, Linglingzhi Zhu, Yao XieGenerative Modeling Continuous Normalizing Flows
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA
Prior-Fitted Functional Flow: In-Context Generative Models for Pharmacokinetics
Apr 19, 2026César Ojeda, Niklas Hartung, Wilhelm Huisinga +6Zero-Shot Time Series Forecasting Healthcare
University of Potsdam, Germany · Technical University Berlin, Germany · Freie Universität Berlin, Germany +2
Local Flow Matching Generative Models
Oct 3, 2024Chen Xu, Xiuyuan Cheng, Yao XieFlow Matching Generative Modeling
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology. · Department of Mathematics, Duke University
Autonomous-Flow-Based Generation
Date pendingHossein Rouhvarzi, Anastasis KratsiosUniversal Approximation Continuous Normalizing Flows
McMaster University and the Vector Institute Department of Mathematics 1280 Main Street West, Hamilton, Ontario, L8S 4K1, Canada

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Seq-Flow: Efficient Probabilistic Forecasting with Self-Rollout Error Control

Yinan Huang, Shitij Govil, Bo Dai, Pan Li

Georgia Institute of Technology

Probabilistic Forecasting Probabilistic Time Series Forecasting Flow-Based Generative Modeling Continuous Normalizing Flows

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Many scientific forecasting tasks require updating a distribution over future trajectories as new observations arrive. Conventional diffusion and flow models generate each forecast from Gaussian noise, often at the cost of many sampling steps. Warm-start methods reuse earlier predictions to reduce this cost, but their models are not trained to perform the forecast update itself, which can compromise quality under few-step sampling. In this work, we introduce Seq-Flow, a conditional flow model whose ODE transports samples from the previous forecast distribution to the updated one. Because successive forecasts often differ only modestly, this transport starts from an informative distribution and can produce accurate updates with few flow evaluations. Recursive reuse also creates a challenge: errors in one forecast become errors in the initial states of subsequent flows. We address this with self-rollout training, in which a moving average copy of the model generates forecasts that initialize later training updates. Unlike self-forcing methods, which reuse generated outputs as conditioning context, Seq-Flow reuses them as the source of the next flow. Experiments On particle-accelerator beam spill forecasting show Seq-Flow reduces CRPS by 65% under a few-NFE sampling budget, while remaining competitive with strong baselines on fluid-dynamics forecasting tasks. Although trained on self-rollouts of at most four updates, Seq-Flow remains accurate over more than 400 consecutive updates. Our code is available at https://github.com/Graph-COM/Seq-Flow.

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