Generative Flows papers, 2023–2026

Key findings

• Generative Flows went from 73 papers in 2023 to 139 in 2026, roughly 1.9x in three years.
• NeurIPS publishes the most Generative Flows work: 130 papers from 2023 to 2026, 30% of the four-venue total.
• The fastest-growing overlap is with Applications · chemistry physics and earth sciences: papers tagged with both rose from 1 in 2023 to 5 in 2026.
• Among the 5 topics under Probabilistic and Bayesian, it ranks #3 by 2026 paper count; the fastest grower in the group, Gaussian Processes, is up 2.2x versus 1.9x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026Exact Recovery of Lipschitz Orthogonal Coordinate Transformations via Constrained Normalizing Flows
• ICML 2026Learning-to-Optimize via Deep Unfolded Flows
• ICLR 2026Frozen Priors, Fluid Forecasts: Prequential Uncertainty for Low-Data Deployment with Pretrained Generative Models
• CVPR 2026AnchorFlow Training-Free 3D Editing via Latent Anchor-Aligned Flows
• NeurIPS 2025Amortized Sampling with Transferable Normalizing Flows
• ICML 2025Annealing Flow Generative Models Towards Sampling High-Dimensional and Multi-Modal Distributions
• ICLR 2025Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks
• CVPR 2025DiverseFlow: Sample-Efficient Diverse Mode Coverage in Flows
• NeurIPS 2024Learning Distributions on Manifolds with Free-Form Flows
• ICML 2024A Computational Framework for Solving Wasserstein Lagrangian Flows
• ICLR 2024Fast and unified path gradient estimators for normalizing flows
• CVPR 2024Distributionally Generative Augmentation for Fair Facial Attribute Classification
• NeurIPS 2023Canonical normalizing flows for manifold learning
• ICML 2023MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows
• ICLR 2023AE-FLOW: Autoencoders with Normalizing Flows for Medical Images Anomaly Detection
• CVPR 2023Delving Into Discrete Normalizing Flows on SO(3) Manifold for Probabilistic Rotation Modeling
• NeurIPS 2026From Baselines to Transport Geodesics: Axiomatic Attribution via Optimal Generative Flows
• ICML 2026Hyperbolic RQ-VAE enhanced Generative Recommendation with Differential-Length Codebook Strategy
• ICLR 2026JAPAN: Joint Adaptive Prediction Areas with Normalising Flow
• CVPR 2026BUSSARD Normalizing Flows for Bijective Universal Scene-Specific Anomalous Relationship Detection

All Generative Flows papers

429 papers, page 5 of 9

201. ICML 2025Computing Optimal Transport Maps and Wasserstein Barycenters Using Conditional Normalizing Flows
202. ICML 2025Concept-Centric Token Interpretation for Vector-Quantized Generative Models
203. ICML 2025Counterfactual Contrastive Learning with Normalizing Flows for Robust Treatment Effect Estimation
204. ICML 2025Flexible Tails for Normalizing Flows
205. ICML 2025Flowing Datasets with Wasserstein over Wasserstein Gradient Flows
206. ICML 2025Hessian Geometry of Latent Space in Generative Models
207. ICML 2025Importance Corrected Neural JKO Sampling
208. ICML 2025Improving Consistency Models with Generator-Augmented Flows
209. ICML 2025Inductive Moment Matching
210. ICML 2025InfoSEM: A Deep Generative Model with Informative Priors for Gene Regulatory Network Inference
211. ICML 2025Is Noise Conditioning Necessary for Denoising Generative Models?
212. ICML 2025Learning Distances from Data with Normalizing Flows and Score Matching
213. ICML 2025Learning Monotonic Probabilities with a Generative Cost Model
214. ICML 2025Normalizing Flows are Capable Generative Models
215. ICML 2025Revisiting Unbiased Implicit Variational Inference
216. ICML 2025Scalable Equilibrium Sampling with Sequential Boltzmann Generators
217. ICML 2025Score-based Pullback Riemannian Geometry: Extracting the Data Manifold Geometry using Anisotropic Flows
218. ICML 2025Stream-level Flow Matching with Gaussian Processes
219. ICML 2025Temperature-Annealed Boltzmann Generators
220. ICML 2025Testing Conditional Mean Independence Using Generative Neural Networks
221. ICML 2025Training Diffusion-based Generative Models with Limited Data
222. NeurIPS 2025Ambient Diffusion Omni: Training Good Models with Bad Data
223. NeurIPS 2025Amortized Sampling with Transferable Normalizing Flows
224. NeurIPS 2025Amortized Variational Transdimensional Inference
225. NeurIPS 2025Asymptotically exact variational flows via involutive MCMC kernels
226. NeurIPS 2025Balanced Conic Rectified Flow
227. NeurIPS 2025Beyond Scores: Proximal Diffusion Models
228. NeurIPS 2025CDFlow: Building Invertible Layers with Circulant and Diagonal Matrices
229. NeurIPS 2025Conformal Prediction Beyond the Seen: A Missing Mass Perspective for Uncertainty Quantification in Generative Models
230. NeurIPS 2025Counterfactual Identifiability via Dynamic Optimal Transport
231. NeurIPS 2025DiCoFlex: Model-Agnostic Diverse Counterfactuals with Flexible Control
232. NeurIPS 2025Edit Flows: Variable Length Discrete Flow Matching with Sequence-Level Edit Operations
233. NeurIPS 2025Enhancing Consistency of Flow-Based Image Editing through Kalman Control
234. NeurIPS 2025Finding separatrices of dynamical flows with Deep Koopman Eigenfunctions
235. NeurIPS 2025Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows
236. NeurIPS 2025FLOWING: Implicit Neural Flows for Structure-Preserving Morphing
237. NeurIPS 2025From Information to Generative Exponent: Learning Rate Induces Phase Transitions in SGD
238. NeurIPS 2025Generative Distribution Embeddings
239. NeurIPS 2025Gradient Variance Reveals Failure Modes in Flow-Based Generative Models
240. NeurIPS 2025Hessian-guided Perturbed Wasserstein Gradient Flows for Escaping Saddle Points
241. NeurIPS 2025Learning to Integrate Diffusion ODEs by Averaging the Derivatives
242. NeurIPS 2025MANGO: Multimodal Attention-based Normalizing Flow Approach to Fusion Learning
243. NeurIPS 2025Mean Flows for One-step Generative Modeling
244. NeurIPS 2025Missing Data Imputation by Reducing Mutual Information with Rectified Flows
245. NeurIPS 2025Multitask Learning with Stochastic Interpolants
246. NeurIPS 2025Multivariate Latent Recalibration for Conditional Normalizing Flows
247. NeurIPS 2025Normalizing Flows are Capable Models for Continuous Control
248. NeurIPS 2025Partial Information Decomposition via Normalizing Flows in Latent Gaussian Distributions
249. NeurIPS 2025Path Gradients after Flow Matching
250. NeurIPS 2025Riemannian Consistency Model

FAQ

How many Generative Flows papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

429 in total: 73 in 2023, 98 in 2024, 119 in 2025, 139 in 2026.

How many Generative Flows papers are at NeurIPS 2026?

NeurIPS 2026 has 46 Generative Flows papers, versus 37 the year before.

How many Generative Flows papers are at ICLR 2026?

ICLR 2026 has 37 Generative Flows papers, versus 35 the year before.

How many Generative Flows papers are at ICML 2026?

ICML 2026 has 32 Generative Flows papers, versus 25 the year before.

How many Generative Flows papers are at CVPR 2026?

CVPR 2026 has 24 Generative Flows papers, versus 22 the year before.

Which conference publishes the most Generative Flows papers?

NeurIPS, with 130 papers from 2023 to 2026.