Key papers

• A Structure-Adaptive Random Feature Method for High-Dimensional Elliptic PDEs (2026)
  Jiale Linghu et al.
  4.39
• Hard Guarantees at a Measured Price: Entropy-Stable Learned Finite Volumes for Compressible Flow (2026)
  Denis Gueyffier (ONERA -- Institut Polytechnique de Paris)
  4.39
• PG-KINN: A Physics-Informed Petrov-Galerkin Kolmogorov-Arnold Network for Solving Forward and Inverse PDEs (2026)
  Amirhossein Sadr et al.
  4.39
• In-span learning: adapting reduced-order models using their own predictions (2026)
  Amirpasha Hedayat et al.
  2.00
• From Core to Detail: Unsupervised Disentanglement with Entropy-Ordered Flows (2026)
  Daniel Galperin et al.
  1.72
• Flow Matching from Viewpoint of Proximal Operators (2026)
  Kenji Fukumizu et al.
  1.72
• Variational Autoencoder with Normalizing flow for X-ray spectral fitting (2026)
  Fiona Redmen et al.
  1.67
• Stability of the Kim--Milman flow map (2025)
  Sinho Chewi et al.
  1.56
• Tensor Gauge Flow Models (2025)
  Alexander Strunk et al.
  1.56
• Adapting Noise to Data: Generative Flows from 1D Processes (2025)
  Jannis Chemseddine et al.
  1.50
• Robust Optimization in Causal Models and G-Causal Normalizing Flows (2025)
  Gabriele Visentin and Patrick Cheridito
  1.50
• Balanced conic rectified flow (2025)
  Shin Seong Kim et al.
  1.50
• Asymptotically exact variational flows via involutive MCMC kernels (2025)
  Zuheng Xu et al.
  1.28
• Diffeomorphic Reconstruction Of A 2D Simple Non Parametric Manifold From Level Set Data Via Shape Gradients (2025)
  Shafeequdheen P et al.
  1.22
• Identifying Memorization of Diffusion Models through $p$-Laplace Analysis: Estimators, Bounds and Applications (2025)
  Jonathan Brokman et al.
  1.22
• Model-Informed Flows for Bayesian Inference (2025)
  Joohwan Ko et al.
  1.22
• Transport f divergences (2025)
  Wuchen Li
  1.17
• A Neural Difference-of-Entropies Estimator for Mutual Information (2025)
  Haoran Ni and Martin Lotz
  1.06
• Improving Variational Inference with Inverse Autoregressive Flow (2016)
  Diederik P. Kingma et al.
  —
• Probabilistic Diffeomorphic Registration: Representing Uncertainty (2017)
  Demian Wassermann (ATHENA) et al.
  —
• Multiplicative Normalizing Flows for Variational Bayesian Neural Networks (2017)
  Christos Louizos and Max Welling
  —
• Improving Variational Auto-Encoders using convex combination linear Inverse Autoregressive Flow (2017)
  Jakub M. Tomczak and Max Welling
  —
• Variational Methods for Normal Integration (2017)
  Yvain Qu\'eau et al.
  —
• Convolutional Normalizing Flows (2017)
  Guoqing Zheng et al.
  —
• Shape optimization in laminar flow with a label-guided variational autoencoder (2017)
  Stephan Eismann et al.
  —
• Sylvester Normalizing Flows for Variational Inference (2018)
  Rianne van den Berg and Leonard Hasenclever and Jakub M. Tomczak and Max Welling
  —
• Non-convex non-local flows for saliency detection (2018)
  Iv\'an Ram\'irez et al.
  —
• On Variational Methods for Motion Compensated Inpainting (2018)
  Francois Lauze and Mads Nielsen
  —
• Understanding MCMC Dynamics as Flows on the Wasserstein Space (2019)
  Chang Liu et al.
  —
• A total variation based regularizer promoting piecewise-Lipschitz reconstructions (2019)
  Martin Burger et al.
  —
• Fast Flow Reconstruction via Robust Invertible nxn Convolution (2019)
  Thanh-Dat Truong et al.
  —
• Neural Spline Flows (2019)
  Conor Durkan et al.
  —
• Approximate Bayesian Computation with the Sliced-Wasserstein Distance (2019)
  Kimia Nadjahi et al.
  —
• MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference (2020)
  Achille Thin et al.
  —
• Uncertainty quantification in medical image segmentation with normalizing flows (2020)
  Raghavendra Selvan et al.
  —
• Ordering Dimensions with Nested Dropout Normalizing Flows (2020)
  Artur Bekasov et al.
  —
• Density Deconvolution with Normalizing Flows (2020)
  Tim Dockhorn et al.
  —
• Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization (2020)
  Abhinav Agrawal et al.
  —
• Variational Inference with Continuously-Indexed Normalizing Flows (2020)
  Anthony Caterini and Rob Cornish and Dino Sejdinovic and Arnaud Doucet
  —
• Semi Conditional Variational Auto-Encoder for Flow Reconstruction and Uncertainty Quantification from Limited Observations (2020)
  Kristian Gundersen et al.
  —
• Alternating minimization for a single step TV-Stokes model for image denoising (2020)
  Bin Wu et al.
  —
• Transforming Gaussian Processes With Normalizing Flows (2020)
  Juan Maro\~nas et al.
  —
• Variational Determinant Estimation with Spherical Normalizing Flows (2020)
  Simon Passenheim and Emiel Hoogeboom
  —
• Trumpets: Injective Flows for Inference and Inverse Problems (2021)
  Konik Kothari et al.
  —
• Maximal function pooling with applications (2021)
  Wojciech Czaja et al.
  —
• Universal Approximation of Residual Flows in Maximum Mean Discrepancy (2021)
  Zhifeng Kong et al.
  —
• E(n) Equivariant Normalizing Flows (2021)
  Victor Garcia Satorras et al.
  —
• Multi-Resolution Continuous Normalizing Flows (2021)
  Vikram Voleti et al.
  —
• On the expressivity of bi-Lipschitz normalizing flows (2021)
  Alexandre Verine et al.
  —
• Half-Space and Box Constraints as NUV Priors: First Results (2021)
  Raphael Keusch et al.
  —
• Variational Wasserstein gradient flow (2021)
  Jiaojiao Fan et al.
  —
• Funnels: Exact maximum likelihood with dimensionality reduction (2021)
  Samuel Klein et al.
  —
• Density reconstruction from schlieren images through Bayesian nonparametric models (2022)
  Bryn Noel Ubald (1) et al.
  —
• Stochastic normalizing flows as non-equilibrium transformations (2022)
  Michele Caselle et al.
  —
• Nonlinear Isometric Manifold Learning for Injective Normalizing Flows (2022)
  Eike Cramer et al.
  —
• Visualizing Riemannian data with Rie-SNE (2022)
  Andri Bergsson et al.
  —
• VQ-Flows: Vector Quantized Local Normalizing Flows (2022)
  Sahil Sidheekh et al.
  —
• Discretely Indexed Flows (2022)
  Elouan Argouarc'h et al.
  —
• MixFlows: principled variational inference via mixed flows (2022)
  Zuheng Xu et al.
  —
• Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows (2022)
  Feynman Liang et al.
  —