Key papers

• On the Role of Normalization in Binary Iterative Hard Thresholding for 1-bit Compressed Sensing (2026)
  Arya Mazumdar et al.
  5.01
• Minimax Theory of Likelihood-Based Deep Learning for Speckle Regression (2026)
  Soham Jana
  4.39
• Non-Asymptotic Variational Learning for Monotone Nonlinear Multiscale Elliptic Equations: Scale-Robust Primal-Dual Bounds and Strong-Form Statistical Ill-Conditioning (2026)
  Ronald Katende
  4.39
• 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
• A Mixture Autoregressive Image Generative Model on Quadtree Regions for Gaussian Noise Removal via Variational Bayes and Gradient Methods (2026)
  Shota Saito et al.
  1.89
• Beyond ReinMax: Low-Variance Gradient Estimators for Discrete Latent Variables (2026)
  Daniel Wang and Thang D. Bui
  1.78
• SPDE Methods for Nonparametric Bayesian Posterior Contraction and Laplace Approximation (2026)
  Enric Alberola-Boloix et al.
  1.78
• Variational inference via radial transport (2026)
  Luca Ghafourpour 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
• A Perceptually Inspired Variational Framework for Color Enhancement (2025)
  Rodrigo Palma-Amestoy et al.
  1.56
• Adapting Noise to Data: Generative Flows from 1D Processes (2025)
  Jannis Chemseddine et al.
  1.50
• A Compositional Kernel Model for Feature Learning (2025)
  Feng Ruan et al.
  1.44
• Optimization Guarantees for Square-Root Natural-Gradient Variational Inference (2025)
  Navish Kumar et al.
  1.33
• Asymptotically exact variational flows via involutive MCMC kernels (2025)
  Zuheng Xu et al.
  1.28
• Semi-Implicit Variational Inference via Kernelized Path Gradient Descent (2025)
  Tobias Pielok et al.
  1.28
• Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation (2025)
  Ronald Katende
  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
• Transport f divergences (2025)
  Wuchen Li
  1.17
• A Relaxed Wasserstein Distance Formulation for Mixtures of Radially Contoured Distributions (2025)
  Keyu Chen et al.
  1.11
• Procrustes Wasserstein Metric: A Modified Benamou-Brenier Approach with Applications to Latent Gaussian Distributions (2025)
  Kevine Meugang Toukam
  1.11
• Image Decomposition with G-norm Weighted by Total Symmetric Variation (2025)
  Roy Y. He et al.
  1.11
• A Neural Difference-of-Entropies Estimator for Mutual Information (2025)
  Haoran Ni and Martin Lotz
  1.06
• Overdispersed Black-Box Variational Inference (2016)
  Francisco J. R. Ruiz et al.
  —
• Relevant sparse codes with variational information bottleneck (2016)
  Matthew Chalk et al.
  —
• Improving Variational Inference with Inverse Autoregressive Flow (2016)
  Diederik P. Kingma et al.
  —
• The Generalized Reparameterization Gradient (2016)
  Francisco J. R. Ruiz and Michalis K. Titsias and David M. Blei
  —
• A Variational Bayesian Approach for Image Restoration. Application to Image Deblurring with Poisson-Gaussian Noise (2016)
  Yosra Marnissi et al.
  —
• Fast Bayesian Non-Negative Matrix Factorisation and Tri-Factorisation (2016)
  Thomas Brouwer et al.
  —
• Variational Fourier features for Gaussian processes (2016)
  James Hensman et al.
  —
• Probabilistic Diffeomorphic Registration: Representing Uncertainty (2017)
  Demian Wassermann (ATHENA) et al.
  —
• An Edge Driven Wavelet Frame Model for Image Restoration (2017)
  Jae Kyu Choi et al.
  —
• Multiplicative Normalizing Flows for Variational Bayesian Neural Networks (2017)
  Christos Louizos and Max Welling
  —
• On parameters transformations for emulating sparse priors using variational-Laplace inference (2017)
  Jean Daunizeau
  —
• Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference (2017)
  Geoffrey Roeder et al.
  —
• Kernel Implicit Variational Inference (2017)
  Jiaxin Shi et al.
  —
• 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.
  —
• Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment (2017)
  Ruben H\"uhnerbein et al.
  —
• Convolutional Normalizing Flows (2017)
  Guoqing Zheng et al.
  —
• A novel total variation model based on kernel functions and its application (2017)
  Zhizheng Liang et al.
  —
• Convergence Rates of Variational Posterior Distributions (2017)
  Fengshuo Zhang and Chao Gao
  —
• Shape optimization in laminar flow with a label-guided variational autoencoder (2017)
  Stephan Eismann et al.
  —
• Geometrical Insights for Implicit Generative Modeling (2017)
  Leon Bottou and Martin Arjovsky and David Lopez-Paz and Maxime Oquab
  —
• Gauged Mini-Bucket Elimination for Approximate Inference (2018)
  Sungsoo Ahn et al.
  —
• Weighted Nonlocal Total Variation in Image Processing (2018)
  Haohan Li et al.
  —
• Black-box Variational Inference for Stochastic Differential Equations (2018)
  Thomas Ryder et al.
  —
• Sylvester Normalizing Flows for Variational Inference (2018)
  Rianne van den Berg and Leonard Hasenclever and Jakub M. Tomczak and Max Welling
  —
• Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models (2018)
  Hugh Salimbeni et al.
  —
• Non-convex non-local flows for saliency detection (2018)
  Iv\'an Ram\'irez et al.
  —
• Wasserstein Variational Inference (2018)
  Luca Ambrogioni et al.
  —
• Variational Wasserstein Clustering (2018)
  Liang Mi et al.
  —
• On Variational Methods for Motion Compensated Inpainting (2018)
  Francois Lauze and Mads Nielsen
  —
• Variance reduction properties of the reparameterization trick (2018)
  Ming Xu et al.
  —
• The Deep Weight Prior (2018)
  Andrei Atanov et al.
  —
• Low-Rank Phase Retrieval via Variational Bayesian Learning (2018)
  Kaihui Liu et al.
  —